surfactant mediated passivation to achieve chemical mechanical...
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SURFACTANT MEDIATED PASSIVATION TO ACHIEVE CHEMICAL MECHANICAL POLISHING SELECTIVITY
By
KYOUNG-HO BU
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2007
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© 2007 Kyoung-Ho Bu
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To my beloved family, Mineok, Minji, and Seongah Byeon.
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ACKNOWLEDGMENTS
It is a privilege to work with intelligent and committed individuals. Too many people to
mention have influenced my work and provided inspiration and useful suggestions over many
years, but I would especially like to express my appreciation to my advisor, Dr. Brij Moudgil, for
his invaluable research guidance and constructive support through intense discussions and
productive feedback on this study. His sincere dedication to science, discipline in conducting
research and considerate attention to details have always kept me moving forward and made
significant contributions to this dissertation.
I would also like to acknowledge the other members of my advisory committee, Dr. Rajiv
Singh, Dr. Stephen Pearton, Dr. Dinesh Shah, and Dr. Wolfgang Sigmund, for their
indispensable support. I also wish to acknowledge Dr. Susan Sinnott, Dr. Chang-Won Park, Dr.
Yakov Rabinovich, Dr. Ivan Vakarelski, Dr. Parvesh Sharma, and Dr. Manoj Varshney who
have informed and elaborated this work, with special appreciation to Dr. Ko Higashitani for his
valuable insights.
I am grateful to the National Science Foundation’s Engineering Research Center for
Particle Science and Technology for financially supporting this research (Grant EEC-94-02989).
To Gary Schieffele, Gill Brubaker, and all other ERC staff, faculty, and administrators, I extend
my hearty thanks for making my time there productive.
Colleagues and friends who have contributed to this research through critical discussions
as well as friendship include Scott Brown, Vijay Krishna, Madhavan Esayanur, Rhye Hamey,
Marie Kissinger, Monica James, Dushyant Shekhawat, Suresh Yeruva, Kalyan Gokhale, Amit
Singh, Debamitra Duta, Stephen Tedeschi, Sejin Kim, Takgeun Oh, Sangyup Kim, Won-Seop
Choi, Seung-Mahn Lee, Kyo-Se Choi, Suho Jung, and Inkuk Jun. I also thank Bryce Devine and
Bryan Op’t Holt for training me how to use modeling tools.
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I have been blessed with Father Sangsun Park in Gainesville Korean Catholic Church who
helps me have peace in mind, and blessed with my children, Minseok and Minji, who encourage
me to overcome obstacles and motivate me to try my best in life. In addition, I owe particular
debts to my parents and my parents-in-law for their strong confidence in my family.
Finally, I am always grateful to my wife, Seongah, for her patience and support in spite of
all ups and downs during my study. This work would not have been possible without her.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS ...............................................................................................................4
LIST OF TABLES...........................................................................................................................8
LIST OF FIGURES .........................................................................................................................9
ABSTRACT...................................................................................................................................13
CHAPTER
1 INTRODUCTION ..................................................................................................................15
2 LITERATURE REVIEW .......................................................................................................21
Shallow Trench Isolation (STI) Structure and Selectivity of Slurry ......................................21 Influence of Selectivity on Global Planarization in STI CMP Process ..................................22 Nanotopography .....................................................................................................................24 Surfactant Mediated Lubrication Effects................................................................................24 Surface Chemical Characteristics of SiO2 and Si3N4 Surfaces in Aqueous Solution.............25 Surfactants Adsorption on Silicon Nitride and Lubrication Effect ........................................26 Mixed Surfactants System ......................................................................................................27 Research Approach.................................................................................................................28
3 CMP CHARACTERISTICS OF SILICA AND SILICON NITRIDE...................................37
Experimental...........................................................................................................................38 Relationship between Material Removal Rate (MRR) and Young’s Modulus ......................39 Role of Electrostatic Interactions on MRR.............................................................................41
Effect of pH ....................................................................................................................42 Effect of Salt Addition ...................................................................................................45
Parameters Affecting Surface Finish in STI CMP .................................................................48 Salt Mediated Lubrication ......................................................................................................51
4 ROLE OF SURFACTANTS IN DEVLOPING SELECTIVE PASSIVATION LAYER IN CMP...................................................................................................................................72
High Selectivity Slurry Using Surfactants..............................................................................73 Surfactant Mediated Boundary Layer Lubrication for Selective Polishing............................75 Optimization of High Selectivity Slurry.................................................................................76
5 ADSORPTION STUDY OF SODIUM DODECYL SULFATE ON SILICA ......................86
Adsorption Behavior of SDS on Silica...................................................................................87 Structure of Adsorbed SDS Molecules...................................................................................89
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6 APPLICATION OF DENSITY FUNTIONAL THEORY BASED MODELING FOR SURFACTANT ADSORPTION STUDY ...........................................................................100
Methodologies ......................................................................................................................101 Structures and Resources......................................................................................................104 Results and Discussion .........................................................................................................106
SDS Adsorption on Silica at, below, and above the Isoelectric Point (IEP) .................107 SDS Adsorption on Silicon Nitride at IEP ....................................................................108 TX-100 Adsorption on Silica at IEP .............................................................................109
7 CONCLUSIONS AND SUGGESTIONS FOR FUTURE WORK......................................120
Conclusions...........................................................................................................................120 Suggestions for Future Work................................................................................................122
LIST OF REFERENCES.............................................................................................................125
BIOGRAPHICAL SKETCH .......................................................................................................133
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LIST OF TABLES
Table page 1-1 A Product Generations and Chip Size Model Technology Trend Targets—Near-term
Years ..................................................................................................................................17
3-1 Young’s modulus, hardness measured by nanoindentation method, material removal rate (MRR), ratio of MRR (CMP pressure of 7 psi), and ratio of Young’s modulus for silica and silicon nitride................................................................................................54
6-1 Adsorption energy (kcal/mol) calculated by density functional theory (DFT) based method (B3LYP) using 6-31G* basis set. .......................................................................111
6-2 Adsorption free energy (kcal/mol) of SDS on silica calculated from adsorption density data in Ch. 5 at different pH and two different added concentrations (1.6mM and 16mM). .....................................................................................................................112
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LIST OF FIGURES
Figure page 1-1. Schematic representation of chemical mechanical polishing (CMP) process. .................18
1-2. Moore's Law Means More Performance............................................................................19
1-3. Multilevel metallization, cross section with silica dielectric and aluminum metallization.......................................................................................................................20
2-1. Schematic shallow isolation structure................................................................................29
2-2. Nanotopography (a) Top view and (b) cross-section graph of substrate nanotopography..................................................................................................................30
2-3. In-situ friction force and material removal rate responses of the baseline slurries (12 wt%, 0.2 mm primary particle size) and the slurries containing C12TAB, C10TAB and C8TAB surfactants at 32, 68 and 140 mM concentrations in the presence of 0.6 M NaCl at pH 10.5. ................................................................................................................31
2-4. Lateral force as a function of loading force in the presence of surfactant [22]. ................32
2-5. Zeta potential behavior of silica, silicon nitride, cerium oxide (ceria), and polishing pad (polyurethane) with respect to the pH . .......................................................................33
2-6. Maximum surface concentration of benzoic acid (●) and pyridine (□) obtained by fitting the adsorption data to a Langmuir-Freundlich equation. ........................................34
2-7. Friction coefficient of silicon nitride ceramic as a function of load in pure water (○) and silane aqueous solution (●) .........................................................................................35
2-8. The mechanism of high-ionic-strength slurry stabilization by the synergistic mixture of anionic and nonionic surfactants ...................................................................................36
3-1. Variations of mateiral removal rate (MRR) for silica and silicon nitride substrate as a function of applied pressure by using undiluted (30 wt%) colloidal silica slurry at pH 10.4.....................................................................................................................................55
3-2. Variations of MRR of silica and silicon nitride substrate and calculated electrostatic force between two abrasives as a function of pH of the diluted (12 wt%) colloidal silica-based slurry (Klebosol 1501-50)..............................................................................56
3-3. Particle size distributions of colloidal silica slurry at two different pH conditions...........57
3-4. Zeta potential of colloidal silica slurry and electrostatic force between silica abrasive particles. .............................................................................................................................58
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3-5. Variations of MRR and calculated electrostatic force between two abrasives as a function of slurry NaCl salt concentrations in the slurry at pH 10.4. ................................59
3-6. Particle size distributions of colloidal silica slurry (Klebosol 1501-50, 12 wt%) as a function of salt concentrations at pH 10.4. ........................................................................60
3-7. Surface roughness of silica and silicon nitride substrate after CMP as a function of added salt (NaCl) concentration at pH 10.4.......................................................................61
3-8. Material removal rate of silica and silicon nitride as a function of repulsive electrostatic force between silica abrasives. ......................................................................62
3-9. Surface roughness of silica and silicon nitride substrates after CMP as a function of slurry pH. ...........................................................................................................................63
3-10. Surface morphologies and profiles of substrates from two pH conditions........................64
3-11. Material thickness change of silica and silicon nitride substrates as a function of immersed time in pH 13 NaOH solution. ..........................................................................65
3-12. Surface morphologies and profiles of substrates before and after etching in pH 13 NaOH solutions..................................................................................................................66
3-13. Etch pits formed on (a) silica and (b) silicon nitride substrate immersed in 0.1 M (pH 13) NaOH solution for 12 days..........................................................................................67
3-14. Lateral force of a 6.8 μm silica particle interacting with a silica substrate in pure water and CsCl, NaCl, and LiCl solutions of 1 M.............................................................68
3-15. Schematic representation of the hypothetical frictional mechanisms................................69
3-16. Particle size distributions of colloidal silica slurry (Fuso PL-7) without salt and with 1 M LiCl and 1 M CsCl. ....................................................................................................70
3-17. Material removal rate of silica substrates by CMP using diluted (9.6 wt%) colloidal silica slurries (PL-7) without salt and with 1 M LiCl and 1 M CsCl as a function of applied polishing pressure..................................................................................................71
4-1. Influence of SDS addition on CMP performances. ...........................................................79
4-2. Surface finish of silica and silicon nitride substrates processed with standard and high selectivity slurry.........................................................................................................80
4-3. Variation of zeta potential of silica and silicon nitride substrate and adsorption density of 16mM SDS on silica and silicon nitride powder measured by total organic carbon (TOC). ....................................................................................................................81
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4-4. Variation of MRR and accompanying selectivity of Klebosol slurry (12 wt%) as a function of added SDS concentration at pH 2. ..................................................................82
4-5. Adsorption density of SDS on 12 wt% Klebosol slurry with 16 mM SDS as a function of pH. ...................................................................................................................83
4-6. Effect of alkyl chain length of sodium alkyl sulfate on MRR and selectivity at pH 2. .....84
4-7. MRR and selectivity obtained by slurries with various surfactant and surfactant mixtures at pH 2.................................................................................................................85
5-1. Adsorption isotherm of SDS on colloidal silica (Klebosol 1501-50, 12 wt%) at pH 10.4.....................................................................................................................................93
5-2. Adsorption density of SDS on colloidal silica (12 wt% Klebosol 1501-50) at SDS concentration of 1.6 mM and 16 mM and zeta potential as a function of pH. ..................94
5-3. Zeta potential of Klebosol slurry as a function of SDS concentration at pH 10.4.............95
5-4. Pictorial depictions of the possible surfactant aggregates films at concentrations corresponding to I-IV in Figure 5-3...................................................................................96
5-5. Adsorption characteristics of SDS on Klebosol silica slurry and zeta potential as a function of concentration of SDS at pH 10.4.....................................................................97
5-6. FTIR/ATR Spectra of SDS solution at 1, 2.5, 5 and 10 mM bulk concentration in the CH2 stretching region (2921, 2924) measured at pH 10.4 using Si ATR crystal. .............98
5-7. Particle size distribution of Geltech SiO2 at pH 2 with and without 16 mM SDS 12 hours after pH change. .......................................................................................................99
6-1. Optimized (a) Si(OH)4, (b) Si(NH2)4, (c) Sodiumdodecyl sulfate (SDS), and (d) Triton X-100 (TX-100) structure using B3LYP method and 6-31G* basis set...............113
6-2. Optimized SiOH4 and DS- complex structure using B3LYP method and 6-31G* basis set. ....................................................................................................................................114
6-3. Optimized SiOH5+ and DS- complex structure using B3LYP method and 6-31G*
basis set. ...........................................................................................................................115
6-4. Sturcture of SiO4H3- and DS- complex. Optimization is not complete, since two
molecules are being separated to decrease energy...........................................................116
6-5. Optimized SiO4H3-, Na+, and DS- complex structure using B3LYP method and 6-
31G* basis set. .................................................................................................................117
6-6. Optimized Si(NH2)4 and DS- complex structure using B3LYP method and 6-31G* basis set. ...........................................................................................................................118
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6-7. Optimized SiOH4 and TX-100 complex structure using B3LYP method and 6-31G* basis set. ...........................................................................................................................119
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
SURFACTANT MEDIATED PASSIVATION TO ACHIEVE CHEMICAL MECHANICAL POLISHING SELECTIVITY
By
Kyoung-Ho Bu
May 2007
Chair: Brij M. Moudgil Major: Materials Science and Engineering
Chemical mechanical polishing (CMP) is an indispensable technique in the
microelectronics industry to achieve planarization and patterning of metal and dielectric layers.
Device fabrication using high density and small pattern size requires precise control of CMP
slurry properties.
In this study, the performance of a colloidal silica CMP slurry for silica/silicon nitride,
which consists of the shallow trench isolation (STI) structures, was investigated. Factors
determing material removal rate and surface finish were examined. It was found that electrostatic
interactions can have significant effects on CMP performance. Emphasis was placed on selective
removal of material. More than 10-fold increase in selectivity over conventional colloidal silica
slurry was achieved with the addition of sodium dodecyl sulfate (SDS), an anionic surfactant.
Adsorption characteristics of SDS on silica and silicon nitride were measured as a function of
slurry pH and surfactant concentration. It was determined that the preferential adsorption of SDS
on silicon nitride by electrostatic attraction results in the formation of a material-selective self-
assembled passivation (boundary lubrication) layer leading to selective polishing. It was found
that the adsorption density of surfactant plays a dominant role in determining selectivity.
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Accordingly, material-targeted boundary layer lubrication concept may be used to develop
selective CMP polishing slurries.
A theoretical approach based on density function theory was attempted to model various
aspects of surfactant adsorption. Through this approach, it was possible to predict adsorption
behavior and related thermodynamic properties to assist selection of passivating molecules.
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CHAPTER 1 INTRODUCTION
Chemical mechanical polishing (CMP) is the planarization technique predominantly used
for the fabrication of multilayer devices. Main components for CMP process include the
substrate to be polished, the slurry that provides the chemistry and abrasives for mechanical
removal-and the polishing pad. A schematic of CMP system is shown in Figure 1-1. Due to the
demand for the faster and smaller devices, the number of devices (density) on a single wafer is
expected to grow constantly as depicted in Figure 1-2. Accordingly, the size of components of a
device is expected to become smaller as listed in Table 1-1. Hence the requirements for large
scale integration are becoming more challenging.
Current semiconductor devices are composed of multilayers as shown in Figure 1-3. Due
to the planarity requirement for lithography processes, further processing is not possible if the
required planarity is not achieved. In addition, the standard for global planarization is becoming
more demanding due to the high degree of device integration.
Among the various structures requiring CMP, shallow trench isolation (STI) is one of the
most challenging, due to its large variation in pattern density. There are a number of possible
approaches to accomplish global planarization in STI CMP process. Among these, the
development of high selectivity slurries has been gaining more significance in order to
accomplish a one-step CMP process for global planarization. State of the art, high selectivity
ceria based slurry has several drawbacks such as problems with coagulation and high defectivity,
whereas conventional silica based slurries are known to be free of those problems, but they
exhibit low polishing selectivity between silicon nitride and silica substrates. In this dissertation,
silica based slurries were modified to achieve the targeted selectivity of 15 or higher.
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The overall objective of the proposed investigation is to improve the selectivity (ratio of
material removal rate of silica to silicon nitride) of the STI CMP slurry. Specific objective is to
differentially modify surface states of silicon nitride and silica with surfactant or polymer
adsorption, thereby selectively minimizing silicon nitride polishing, and thus leading to enhanced
global planarization in STI CMP process. A synopsis of the various research tasks constituting
this study is organized as follows.
Chapter 2 reviews the literature on the STI CMP process and slurry selectivity. Different
defects, hampering device performance will be addressed. The selectivity of the CMP slurry will
be defined and its effect on global planarization will be discussed. Finally, strategies to increase
the selectivity will be suggested. Chapter 3 covers the CMP characteristics of silica and silicon
nitride substrates by colloidal silica slurry with respect to the material removal rate (MRR) and
surface finish. Variables affecting the polishing process have been studied with special emphasis
on electrostatic interactions. Chapter 4 presents the methodologies to increase the selectivity of
the slurry. Specific mechanisms for observed results will be discussed. Chapter 5 discusses the
adsorption behavior of sodium dodecyl sulfate (SDS) on silica substrates, since it was found that
SDS adsorption on silica abrasive particles determines the necessary dosage of surfactant to
fabricate high selectivity slurries. Chapter 6 describes the modeling efforts to develop
methodologies based on density functional theory to predict optimal conditions for selective
surfactant coating. Chapter 7 summarizes the conclusions of this study and suggests future work.
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Table 1-1 A Product Generations and Chip Size Model Technology Trend Targets—Near-term
Years [1]. Year of Production 2005 2006 2007 2008 2009 2010 2011 2012 2013
DRAM ½ Pitch (nm) (contacted) 80 70 65 57 50 45 40 36 32
MPU/ASIC Metal 1 (M1) ½ Pitch (nm) 90 78 68 59 52 45 40 36 32
MPU Printed Gate Length (nm) 54 48 42 38 34 30 27 24 21
MPU Physical Gate Length (nm) 32 28 25 23 20 18 16 14 13
ASIC/Low Operating Power Printed Gate Length (nm) 76 64 54 48 42 38 34 30 27
ASIC/Low Operating Power Physical Gate Length (nm) 45 38 32 28 25 23 20 18 16
Flash ½ Pitch (nm) (un-contacted Poly)(f) 76 64 57 51 45 40 36 32 28
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Figure 1-1. Schematic representation of chemical mechanical polishing (CMP) process. (a) Side
view; (b) Top view.
(b) (a)
Slurry Feed Holder
Substrate
Platen
Polishing Pad
HolderSlurry Feed
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Figure 1-2. Moore's Law Means More Performance. Processing power, measured in millions of
instructions per second (MIPS), has steadily risen because of increased transistor counts [2].
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Figure 1-3. Multilevel metallization, cross section with silica dielectric and aluminum metallization [3].
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CHAPTER 2 LITERATURE REVIEW
Shallow Trench Isolation (STI) Structure and Selectivity of Slurry
Chemical mechanical polishing or planarization (CMP) is the key technology for shallow
trench isolation (STI) process. STI process can reduce the required area for the device isolation
and give better planarity relative to the local oxidation of silicon (LOCOS) process. Therefore,
existing sub-0.13 μm technologies device isolation techniques strongly depend on the STI CMP
process [4-7].
There are several drawbacks such as dishing of silica, erosion of silicon nitride and failure
to clear oxide that hamper global planarization in CMP process [8]. Typically the thickness
uniformity across the substrate (usually called within-substrate non-uniformity, or WIWNU)
must be below 3%, and dishing must typically be less than 20~50 nm. To minimize such defects,
current STI CMP process is comprised of multi-steps [9] or raw structure modifications such as
reverse mask, dummy active area, and additional active area [10]. For better productivity and
process simplicity, a minimum number of process steps are highly desired and accordingly,
approaches for “high selectivity single-step” slurry designs are being widely investigated [11-13].
Usually selectivity represents the ratio of material removal rate (MRR) of silica to silicon nitride:
1)-(2nitrideSilionofrateremovalMaterial
SilicaofrateremovalMaterialySelectivit =
In general, conventional silica abrasive based STI CMP slurry exhibits selectivity in the
range of 3 to 4 [14]. According to the result reported by J. Schlueter, erosion of silicon nitride
could be minimized to less than 100 Å using ceria based high selectivity slurry in a multi-step
STI CMP [15]. Besides the influence on planarization, high selectivity provides enhanced
endpoint detection capability. Generally, if oxide to nitride polishing selectivity is greater than
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15, monitoring substrate carrier motor current can be utilized for endpoint detection [11].
Therefore, research on improving selectivity and understanding the polishing mechanisms to
achieve global planarization are needed. In this study, systematic approaches and strategies to
improve selectivity of the STI CMP slurry for single-step CMP process were investigated.
In the following sections, the detailed influences of selectivity on global planarization
will be introduced, and issues of nanotopography that justify a strong need for high selective STI
CMP process will be outlined. Next, a brief review of the polishing passivation/inhibition
mechanism (i.e., surfactant mediated lubrication effects) will be provided. Surface chemical
characteristics of silica and silicon nitridewill be reviewed, followed by examples of specifically
adsorbing surfactants on silicon nitride surface. As an alternative to inhibit polishing of silicon
nitride by surfactants, silane additives to form passivation layer on silicon nitride will be
introduced.
Influence of Selectivity on Global Planarization in STI CMP Process
As previously mentioned, several obstacles exist inhibiting global planarization in STI
CMP. Figure 2-1 shows a schematic of a typical STI structure. It consists of a silicon device, a
silicon nitride mask, and a silica insulating layer inside of the trenches. In the ideal CMP process,
the oxide should be removed completely in all active regions, leaving it only in the trench
regions (Figure 2-1 (b)) without eroding silicon nitride. In reality, there are three failure modes
such as failure to clear oxide, excessive removal of nitride, and excessive removal of oxide [8].
The former is primarily an end-point detection issue, whereas the other two mechanisms are
closely related to the pattern density of the device, selectivity of slurry, pad stiffness, imposing
pressure, etc. [12]. To minimize these barriers, several approaches have been evaluated. One
method is to use a stiffer pad and lower selectivity slurry [5], and the other is to use a softer pad
and higher selectivity slurry [16]. When a stiffer pad is used, which does not bend in the applied
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pressure range, the highest portion of the surface will start to be polished ultimately resulting in
global planarization. However, there is also a possibility of poor surface finish and wafer
breakage. When a softer pad is used, which has a greater flexibility, all the structures on
substrate will be in contact to pad, and hence a high selectivity slurry will be required not to
preferentially polish the unwanted structure. In this case, the risk of poor surface finish and
substrate breakage will be reduced.
Current high selectivity slurries in STI CMP usually contain ceria abrasives showing
higher material removal rate for silica than silicon nitride [16]. In general, for higher pattern
densities of which the area of silica isolation layer is not large, dishing effect decreases, since the
pad bending is limited. For lower pattern densities, dishing effect increases because the pad
bending is high [17]. Therefore in each case, the pad materials and operating pressure should be
chosen appropriately.
Kim et al. investigated the influence of slurry selectivity of the slurry on erosion and
planarity by modeling. It was predicted that above 30% active pattern density, high selectivity
slurries show good planarity [18]. In these cases, planarity is defined as the difference of height
between the highest region and the lowest region on a substrate. Considering that higher pattern
densities of the device will be required with decreasing device size in the future, a systematic
research for a high selectivity slurry will be essential to meet these goals.
In general, current STI CMP processes use silica abrasives that show low selectivity (about
3 - 4) [14]. W. G. America investigated the influence of selectivity on material removal rate of
silica and silicon nitride using silica and ceria abrasives. In this case, the material removal rate of
silica and silicon nitride was determined to be about 2700 Å/min and 700 Å/min, respectively
[19]. Recently, ceria abrasives have shown higher selectivity (more than 5), and are being
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investigated for further enhancement. According to W. G. America, material removal rate of
silica using ceria abrasives was more than 5700 Å/min as compared to 800 Å/min for silicon
nitride [19]. However in ceria CMP, the pH at which maximum polishing rate and maximum
selectivity are achieved is about 8, which also is the isoelectric point (IEP) of ceria. This results
in coagulation of ceria yielding poor surface morphologies with scratches and higher roughness
[13]. Recently, it has been reported that by decreasing the size of ceria abrasive particles, the
number of scratches can be decreased significantly [20].
Nanotopography
An emerging issue impairing global planarization in STI CMP is nanotopography. This
phenomenon is becoming a strong driving force for developing high selectivity slurries.
Nanotopography is a term used to describe relatively gentle (10-100 nm) surface height
variations occurring over lateral distances of 1-10 mm on unpatterned silicon substrates (Figure
2-2). Boning et al. have investigated this issue by modeling and verified it by experiments, and
have suggested that due to the height variation of blanket wafer, several defect mechanisms
come into play such as failure to clear oxide and excess nitride thinning (erosion). It has been
commonly believed that stiffer pad would yield acceptable planarization [5]. On the contrary, it
has been shown that softer pad and lower pressure is more effective in minimizing such defects
[5].
With respect to this phenomena, if the selectivity of the slurry is not high enough and
endpoint detection is not accurate, accompanying erosion will be unavoidable. Silicon nitride
erosion can be minimized if only additional protective layers exist on silicon nitride surface.
Surfactant Mediated Lubrication Effects
As a protective mechanism from polishing for silicon nitride, one of the approaches is to
incorporate surfactant mediated lubrication effects. Basim et al. have shown that the addition of
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long chain cationic surfactant (e.g. C12TAB) produces an enhanced defect-free surface
morphology but the polishing rate was extremely small due to the lubrication effect of surfactant
(Figure 2-3.) [21]. Although this research was focused on dispersion of abrasive particles, it
implied that long chain surfactant can act as an anti-polishing agent.
Vakarelski et al. showed that the primary mechanism of lubrication is the formation of an
intervening surfactant aggregate film on solid-liquid interface largely by electrostatic interactions
[22]. In addition, the decrease in frictional force depends on the concentration of surfactant.
After the concentration reaches critical micelle concentration (CMC), there was no further
decrease in lateral (frictional) force. The effect of surfactant concentration on the lateral force is
illustrated in Figure 2-4.
Surface Chemical Characteristics of SiO2 and Si3N4 Surfaces in Aqueous Solution
Understanding the surface chemistry of substrates is the first step to implement the above
approach to create a selective passivation/lubrication layer. It is well known that silicon nitride
forms the same type of surface hydroxyl layer as silica in an aqueous solution. However, there is
a difference in the surface group compositions. Figure 2-5 illustrates the zeta potential variation
with respect to pH. Unlike silica (IEP of 2.2), silicon nitride exhibits an IEP of about 5.8. This
difference is explained on the basis of relative number of silanol (Si-OH) and amine (Si2-NH)
groups on the silicon nitride surface as compared to only silanol groups on silica surface [23].
The silanol groups are acidic in nature and thus result in a lower IEP, while the presence of
amine groups results in a higher IEP. In the case of silicon nitride powder with an IEP of pH 6,
the ratio of nitrogen to oxygen was calculated to be approximately 0.2, and it was nearly 1 for
powders with an IEP of pH 7.9 [23].
Sonnefeld et al. reported, based on potentiometric titration measurements, that the surface
site densities of amine group (Si2NH) and that of silanol group (SiOH) are 0.56 /nm2 and 1.83
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/nm2, respectively on the silicon nitride surface [24]. Density of silanol groups on silica surface
was estimated to be 0.74 /nm2 [25]. From these values, converted area of amine and silanol
groups for molecular adsorption is 1.79 nm2 and 0.546 nm2, respectively on the silicon nitride
surface, and area of silanol gropup on silica was 1.35 nm2.
Surfactants Adsorption on Silicon Nitride and Lubrication Effect
There have been many reports on stabilization of silicon nitride powders using polymeric
dispersants [26-30]. Malghan et al. investigated the dispersion behavior of silicon nitride powder
using both cationic - Betz 1190 (quaternized polyamine epoxychlorohydrin) - and anionic -
Darvan C (ammonium poymethacrylate) - polymers [29]. In the case of cationic polyelectrolyte
(CPE), there was strong electrostatic attraction between CPE and silicon nitride powder at pH 9
leading to stable dispersion, while in the case of anionic polyelectrolyte (APE), the adsorption
was very restricted due to the similar surface charge, consequently, small adsorption occurred
possibly due to the hydrogen bonding. According to Hackley et al., anionic poly acrylic acid
(PAA) adsorption on silicon nitride surface decreased from 100% at pH 3 to around 25% at pH
10, however, stable dispersion was achieved due to depletion forces in the presence of PAA [26].
Besides the sign of surface charge, hydrogen bonding plays an important role in adsorption
of organic molecules on silicon nitride. Bergström et al. investigated the adsorption behavior of
various organic probe molecules in cyclohexane [31]. They showed that benzoic acid and benzyl
amine prefer to adsorb on the basic amine (Si2NH) groups via hydrogen bonding (N-H) (Figure
2-6). To accomplish selective adsorption of surfactants or polymers on silicon nitride surface,
anionic surfactants should be investigated first considering that nitride shows higher negative
zeta potential at pH 10.5 for current CMP conditions. Philipossian et al. showed that by applying
anionic poly-carboxylate, the selectivity increased from 5 to 100 [32]. They used ceria abrasives
for silica polishing at pH 8. According to their results, most of anionic surfactant adsorbed on
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silicon nitride with some amount of polymer adsorption on silica and ceria abrasive particles,
resulting in overall decreased MRR from 500 to 100 (a.u.).
Hibi et al. investigated the lubrication effect of silane coupling agents (3-(2-
aminoethylaminopropyl) dimethoxymethylsilane) on silicon nitride and alumina ceramics
(Figure 2-7) [33]. They reported that amino-containing silane coupling agents formed the cross-
linked polysiloxane by hydrolysis and dehydrative condensation, which was effective in reducing
both friction and wear of silicon nitride. In other words, the additives reduced the wear of silicon
nitride as a result of inhibition of silicon nitride reaction with water. In this case, the silane
agents reacted with the oxide (silanol group) on silicon nitride surface.
As mentioned above, since the density of silanol groups on silica and silicon nitride surface
was estimated to be 0.74 /nm2 and 1.83 /nm2, respectively [24, 25], the extent of the passivation
on silicon nitride and silica is expected to be different.
Mixed Surfactants System
Palla et al. investigated the use of mixed surfactants to disperse the alumina abrasive
particles in CMP. They reported that by applying anionic surfactant, sodium dodecyl sulfate
(SDS), mixed with various nonionic surfactants, the dispersion stability was highly improved
[34]. The schematic of the slurry stabilization of alumina abrasives is shown in Figure 2-8. In
this scheme, adsorption was attributed to strong adsorption of ionic surfactants on abrasive
particles, and association of nonionic surfactants with ionic surfactants via hydrocarbon chain
interactions (attractive hydrophobic forces). Alumina is known to have Lewis active site similar
to silicon nitride, hence, it can be envisioned that mixed surfactants concept can be applied to
silicon nitride-silica system. However, under the normal CMP pH conditions, zeta potential of
silicon nitride is negative, indicating the greater significance of electrostatic interaction.
28
Research Approach
Commercial ceria abrasive STI CMP slurries with selectivity of about 5 are known to
result in high defectivity and post-CMP cleaning problems, while colloidal silica slurries has a
lower selectivity of 3 to 4, although they exhibit acceptable defectivity. Therefore in the
proposed research, surfactants that selectively adsorb on silicon nitride will be investigated and
methods to inhibit polishing and the mechanisms will be studied to improve global planarity.
One of the major challenges is the fact that both materials have silanol group on their
surfaces in water and show negative zeta potential at the conventional CMP pH of 10.4. The
ideal solution is to find a surfactant, which has selective affinity only to silicon nitride. To
achieve this goal, several anionic surfactants and mixed surfactant systems will be investigated in
terms of adsorption with respect to pH and added surfactant concentration.
In using silica abrasives under current CMP conditions, anionic surfactants adsorption on
abrasive particles will be largely opposed due to the similar (negative) charge of the adsorbate
and adsorbent. Therefore, to increase the amount of surfactant adsorption on silicon nitride,
readjustment of CMP process pH to a lower value may be required. Since pH plays a dominant
role in determining surfactant adsorption through electrostatic interactions, detailed investigation
of the adsorption behavior of the anionic surfactant as a function of pH will be required to
achieve optimal surfactant adsorption.
29
Figure 2-1. Schematic shallow isolation structure: (a) Initial structure before CMP: typical trench isolation structure used to isolate “active” regions on a substrate where devices will be built. The nitride layer has been patterned and a shallow trench etched into the silicon. An oxide has then been deposited into the trench, which also results in overburden oxide above the nitride active areas. (b) ideal result after CMP: the oxide is removed completely in all active regions, leaving oxide only in the trench regions. Three key failure mechanisms may arise: (c) excessive removal (erosion) of nitride in active areas, (d) excess removal of oxide (dishing) within the trench, and (e) failure to clear oxide from nitride active areas [8].
SiO2
(d) Dishing (c) Erosion
(e) Failure to clear oxide
(a) Before CMP
(b) Ideal result after CMP
Si3N4
Si
30
Figure 2-2. Nanotopography (a) Top view and (b) cross-section graph of substrate nanotopography. Dotted line in (a) shows path of scan. The x axis in (b) indicates the distance along the scan path in (a), moving from left to right [8].
WA
FER
HEI
GH
T (n
m)
100
80
60
40
20
0
-20
-40
-60
-80
-100
Nanotopography Length
100 nm
-100 nm
100 nm
-100 nm
100 nm
-100 nm
(a) (b)
31
Figure 2-3. In-situ friction force and material removal rate responses of the baseline slurries (12 wt%, 0.2 mm primary particle size) and the slurries containing C12TAB, C10TAB and C8TAB surfactants at 32, 68 and 140 mM concentrations in the presence of 0.6 M NaCl at pH 10.5. (Striped bars represent the Friction Force responses and the solid bars represent the Removal Rate responses) [21].
32
Figure 2-4. Lateral force as a function of loading force in the presence of surfactant [22].
0
50
100
150
200
250
300
0 500 1000 1500
L o a ding F o rc e (nN )
Late
ral F
orce
(nN
)
Pure Water 1mM C12TAB 8mM C12TAB 16mM C12TAB 32mM C12TAB
33
Figure 2-5. Zeta potential behavior of silica, silicon nitride, cerium oxide (ceria), and polishing pad (polyurethane) with respect to the pH [32].
34
Figure 2-6. Maximum surface concentration of benzoic acid (●) and pyridine (□) obtained by fitting the adsorption data to a Langmuir-Freundlich equation [31].
0 10 20 30 40 50
Max
. sur
face
con
cent
ratio
n (μ
mol
/m2 )
3.5
Amount amino groups (%)
3.0
2.5
2.0
1.50 10 20 30 40 50
Max
. sur
face
con
cent
ratio
n (μ
mol
/m2 )
3.5
Amount amino groups (%)
3.0
2.5
2.0
1.5
3.5
Amount amino groups (%)
3.0
2.5
2.0
1.5
35
Figure 2-7. Friction coefficient of silicon nitride ceramic as a function of load in pure water (○) and silane aqueous solution (●) [33].
36
Figure 2-8. The mechanism of high-ionic-strength slurry stabilization by the synergistic mixture of anionic and nonionic surfactants [34].
37
CHAPTER 3 CMP CHARACTERISTICS OF SILICA AND SILICON NITRIDE
The Shallow trench isolation (STI) chemical mechanical polishing (CMP) process involves
polishing of silica and silicon nitride layer. Therefore, the characteristics of the both materials are
very important for process optimization and overall STI CMP process performance. Besides,
silicon nitride is widely used for various applications such as giant magnetoresistance (GMR)
and ceramic ball bearings making the research on the CMP characteristics of silicon nitride more
significant [35, 36].
There are several abrasives used in STI CMP slurries according to its specific purposes [19,
36, 37]. Among them, colloidal silica is the traditional material, which has long been used for
various applications, and its dispersion stability towards various electrolytes is well documented
[38-41]. A unique property is that it shows high dispersion stability around its isoelectric point
(IEP, pH 2 ~ 4), unlike other materials. It has long been believed that hydration force due to
modified water structure at the silica surface or silanol (SiOH) groups give rise to a repulsive
forces, which is responsible for the observed phenomena [39, 42]. Another explanation is that the
formation of a surface gel layer or short polymer-like hairs protruding from the silica surface can
give rise to steric repulsion [43, 44]. In intermediate pH range, silicic acid chains (-Si(OH)2-O-
Si(OH)2-OH) or siloxane bonds (Si-O-Si) are reported to form silica gel relatively easily by
reaction between acidic ionized silanol (SiO-) and neutral silanol (SiOH). At a higher than pH 10,
colloidal silica shows stable dispersion again through electrostatic repulsion between almost
completely ionized silanol groups. As a result, colloidal silica suspensions are stored and used
usually under high pH conditions. When a lower pH application is required, the pH transition is
performed in a very short time period to avoid gelation.
38
Silica is a promising candidate for the STI CMP due to its high surface quality as
compared to other materials. However, the basic CMP characteristics for silica and silicon nitride,
which consist of the STI structure, are not completely understood. In this chapter, CMP
characteristics of silicon and silicon nitride by colloidal silica abrasives will be discussed with an
emphasis on the electrostatic interactions encountered in the system.
Experimental
The CMP slurry used in this study was Klebosol 1501-50 from Rodel Co. The original
slurry of 30 wt% colloidal silica abrasives was diluted with nano-pure water to 12 wt%. The
slurry pH was measured to be 10.4 after dilution. HCl and KOH solutions were used for further
adjustment of the slurry pH. The study of lubrication by hydrated cations utilized PL-7 supplied
by Fuso Chemical Co., which is originally at 20 wt% colloidal silica abrasives. It was diluted
with nano pure water to 9.6 wt%, with a final slurry pH of 7.3. Salt concentration was controlled
to 1 M by adding the proper amount of 5 M salt solution to the slurry. Concentrated 5 M solution
was prepared with analytical grade LiCl and CsCl purchased from Fisher Scientific Co. Silica
and silicon nitride wafers were purchased from Silicon Quest International. Two μm thickness of
silica thin film was deposited on (100) Si substrate by plasma enhanced chemical vapor
deposition (PECVD) method using Tetra Ethyl Ortho Silicate (TEOS) as a source on (111) Si.
For the silicon niride wafers, 3000 Å thickness silicon nitride film was deposited on the 3000 Å
silica, which was used as a diffusion barrier on (100) Si by low-pressure chemical vapor
deposition (LPCVD) method using dichlorosilane (SiCl2) and ammonia (NH4) as source
materials. IC 1000/Suba IV stacked pads supplied by Rodel Inc. and TegraPol-35 with
TegraForce-5 from Struers Co. tabletop polisher were utilized for CMP purposes. The rotation
speed was controlled to 150 rpm both for the pad and the wafer. Material removal rate (MRR)
was measured using ellipsometry (Woollam EC110 Ellipsometer) by dividing the decrease in
39
thickness by polishing time. In the present study, MRR reproducibility was within ± 5 %. Prior
to each polishing step, the pad underwent 30 seconds of conditioning with diamond conditioner.
The actual time for polishing was controlled to 30 seconds. Young’s modulus and hardness were
measured by Nanoindentation method using Hysitron Triboindenter purchased from Hysitron Co.
Digital Instruments Nanoscope III atomic force microscope was used for the measurement of
surface roughness of substrates after CMP.
Zeta potential of the slurry was measured by Acoustosizer purchased from Colloidal
Dynamics Co. A variation in the zeta potential values (20 mV) at pH 10.4 was observed for
different batches purchased from slurry supplier. A decrease in zeta potential was also observed
with aging time (10 mV upon 1 year aging). Accordingly, zeta potential values at the same pH
were found to be different depending on the batch and aging time. However for a given sample,
the reproducibility of measurement was found to be within ± 3 mV over a month period.
Particle size distribution was measured by Coulter particle size analyzer (Coulter
LS13320). After dissolution, the pictures of the substrate surface were taken by optical
microscopy (Olympus BX60).
Relationship between Material Removal Rate and Young’s Modulus
The MRR of silica and silicon nitride wafers as a function of polishing pressure is plotted
in Figure 3-1. In this experiment, original slurry (30wt% solids loading) was used without further
dilution. The MRR showed a linear relationship with polishing pressure, as predicted by the
empirical Preston equation [45]:
1)-(3ΔtΔsPKMRR p=
where, Kp is Preston coefficient, P is polishing pressure, and Δs is the relative travel between
glass surface and lap over in which the wear occurs (platen speed) during time interval Δt [45].
40
The MRR of silicon nitride was determined to be lower than silica. In CMP of Si-based
materials such as silica and silicon nitride, it is well known that water plays a significant role,
because no material removal occurs in non aqueous medium. It is commonly believed that water
attacks and breaks the siloxane bonds by the following reaction:
2)-(3SiOHSiOHOHSiOSi 2 +=+−−
It has been reported that the hardness of silica decreases to around 50% of the original value in
aqueous systems [46, 47]. The above reaction is believed to be controlled by the diffusion of
water in silica, which in turn affects surface hardness.
There have been several attempts to explain MRR theoretically [45, 48]. One of them is
Cook’s model, assuming Hertzian penetration [45]:
3)-(3ΔtΔsP
E21MRR =
where, E is the Young’s modulus of the material. Considering that the modulus is the resistance
of the material to tensile or compressive deformation, above equation indicates that material with
high modulus should be harder to polish. A more elaborate model incorporating chemical effects
was proposed by Chi-Wen and co-workers [48]:
4)-(3ΔtΔsP)
E1
E1(CMRR
wa
+=
where, C is the coefficient accounting for chemical effect of a slurry and other properties of
CMP consumables, Ea and Ew are the Young’s modulus of abrasive particle and substrate,
respectively. Trends in experimental results with substrates of different moduli were in
aggrement with those predicted by Equation (3-4).
To evaluate the correlation between MRR and mechanical properties of substrate materials,
Young’s modulus and hardness of both substrates were measured by the nano-indentation
41
method and are summarized in Table 3-1. The MRR and Young’s modulus ratio indicated a
correlation between MRR and mechanical properties of the material. However, according to this
explanation, silicon nitride cannot be polished by silica abrasive particles, since silicon nitride
has a higher hardness than silica, in contrast to experimental evidence. In reality, the formation
of a thin silica layer (around 1 nm) on the silicon nitride surface by spontaneous oxidation
represented by the equation below and is expected to influence the polishing characteristics of
silicon nitride [23, 31, 49]
5)-(332243 NH4SiO3OH6NSi +=+
It has been reported that the rate-limiting step for the above reaction is the breakage of Si-N
bonds [50], with relatively faster breakage of Si-O bonds due to diffusion of water. In other
words, the reaction of water with silicon nitride for breaking the Si-N bond is slower than water
diffusion. As a result, the thickness of the newly formed silica layer on silicon nitride will be
very thin compared to that of the silica substrate, thereby resulting in different MRR of the two
substrates. Theoretically, Young’s modulus reflects the bond strength of the material on an
atomic scale [51]. In other words, a higher modulus means stronger bonds, which will be harder
to break.
Role of Electrostatic Interactions on MRR
It has long been observed that MRR is dependent on the pH of the slurry in various
polishing processes including CMP. As was discussed by Choi and co-workers, electrostatic
interactions can influence the CMP performance. However, systematic approaches and
quantitative analysis to explain the effect and modulation have not been attempted until now.
42
Effect of pH
One of the best ways to modulate the electrostatic interaction is to change pH of the slurry.
Colloidal silica slurry is the best candidate for this purpose, since it shows stable dispersion
throughout a wide pH range, if only the pH was adjusted just before polishing. To investigate the
effect of electrostatic interaction on CMP performance, the MRR for both substrates as a
function of slurry pH was measured and plotted in Figure 3-2. Particle size distribution at pH 2
and 10.4 in Figure 3-3 confirmed that there was no measurable coagulation of silica particles at
pH 2.
MRR as a function of pH reached a maximum as slurry pH is reduced. At high pH beyond
11, MRR steeply increased for silica and remained constant for silicon nitride. The CMP results
of silicon and silica as a function of pH were reported by several authors [52-54]. Choi et al.
attributed the increase in MRR at lower pH to the electrostatic attraction between the oppositely
charged silica substrate and silica abrasive particles, and a higher MRR at higher pH to increased
softening of silica induced by its high solubility at higher pH. According to their report, the
electrostatic force between silica particles and substrate showed a maximum around 0.4 mN/m
(force/radius of particle) at pH 10.4. The contact area of the CMP pad and the substrate was
reported to be around 1% due to the asperity characteristics of the pad materials employed in
their study at the same pH [55]. Assuming that half of the individual abrasive particle will be
embedded in the substrate surface and the other half of the particle will be captured by pad
asperities during the CMP process, the contact area will yield the number of particles in contact
with the substrate. If 1% of a 1 × 1 inch wafer is in contact with the abrasive particles, then there
will be approximately 109 particles of diameter of 90 nm in the system. The total electrostatic
force is calculated to be 18 mN. According to experiments in the present study, if one assumes
that there is no electrostatic force contribution at pH 3 (due to its nearly zero value of zeta
43
potential), a pressure caused by repulsive force of 6.85 N on 1 × 1 inch wafer, is required to
make a difference in MRR. This is more than two orders of magnitude difference in electrostatic
force contribution between the abrasive and the wafer. It is, however, possible that induced
repulsion by electrostatic interactions may contribute to lubrication effects. According to Choi,
there was approximately 25% decrease of frictional force between colloidal silica abrasives and
the wafer when the slurry pH was increased from 2 to 10.4. Mahajan also reported that the
frictional force between pad and the wafer decreased at higher pH due to increased electrostatic
repulsion between them [56].
It is well known that in the case of boundary lubrication, friction follows the equation for
interfacial sliding, as proposed by Tabor et al.[57].
6)-(3ASF cfriction =
where, Ffriction is a frictional force, Sc is a critical shear stress that depends on the details of the
interfacial region, and A is the contact area. It is not clear which term is affected by the
electrostatic interaction for the current system. However, it seems reasonable that if electrostatic
repulsion between the abrasive and substrate is high, critical shear stress (Sc) will be reduced,
resulting in overall reduction in the frictional force. On the other hand, surface layer
characteristics can also change upon a shift in pH, resulting in changes in contact area (A)
between the pad and the substrate. Yeruva reported that there was no consistent evidence that the
Young’s modulus of the pad, which is directly related to the contact area, changes with solution
pH.
Recently, Taran et al. have reported that a lubrication effect between silica particles and the
substrate resulted in reduced lateral force at high pH above 9.6, using lateral force microscopy
[58]. Below pH 9.6, there was no noticeable change. They correlated their observations with
44
solubility of silica and formation of surface gel layer, which is believed to form at high pH due to
high solubility [58]. It seems likely that the lubrication phenomena may play a role in explaining
low MRR at high pH, but it is not possible at present to explain high MRR below pH 8.6.
Another possibility is that the electrostatic forces between particles can change the number
of abrasive particles participating in the polishing process, depending upon their
dispersion/coagulation characteristics. It has been generally known that MRR is almost linearly
proportional to solids loading of the slurry [59, 60]. Zeta potential of the abrasive particle will
produce electrostatic repulsive forces that will resist the particles to come within a certain
distance of the substrate resulting in limited number of particles participating in polishing at a
certain pH. The repulsive force can be calculated using simplified Poisson-Boltzman equation
[61]
7)-(3D2oo e2R/F κκψπεε −=
where, F/R is the electrostatic force per particle radius, κ is the Debye-Huckel parameter, ψo is
surface potential, and D is the distance between particles which is assumed to be 1 nm. The
absolute force value can change as a function of distance, but the trend should be similar. Zeta
potential was assumed to be the same as the surface potential, since there were no specific
adsorbing ions in the slurry. Figure 3-4 shows the measured zeta potential of silica and the
corresponding electrostatic force between abrasive particles calculated from the potential as a
function of pH (also plotted in Figure 3-2). At pH around 3 (IEP of silica), the electrostatic force
leveled off and approached zero and MRR for silica also reached a maximum value at pH 3. In
the intermediate pH range (3 ~ 10), MRR and the electrostatic force were inversely proportional
to each other.
45
At pH above 11, the MRR of silica showed a sudden increase, probably related to the
solubility of silica. However, the MRR of silicon nitride, which has a lower solubility than silica,
showed the same trend as electrostatic force. Overall, it appears that there exists an inverse
correlation between the MRR and repulsive electrostatic forces between the abrasive particles.
The zeta potential of the substrate and colloidal silica should be similar, since both
materials are amorphous silica, therefore it may be safe to assume that the calculated electrostatic
force also represents the trend in the force between abrasive particles and substrate. It is clear
that the electrostatic forces induced by zeta potential of various materials has a significant effect
on MRR in terms of (i) opposing force against polishing pressure or (ii) number of particles
participating in the CMP process.
Effect of Salt Addition
It is well known that various salts reduce the surface charge of the particles in a colloidal
system, decreasing the electrostatic repulsion and thereby promoting their coagulation by
attractive van der Waals interactions [38, 39]. The minimum concentration of salt causing
coagulation of particles is called the critical coagulation concentration (CCC). This phenomenon
can be utilized to modulate the electrostatic force in the CMP process. Among various salts,
monovalent ions are most suitable for this purpose in terms of controllability, since multivalent
ions have far lower CCC than monovalent ions. Allen and co-workers have reported that CCC of
NaCl for colloidal silica was around 0.4 M and that of CaCl2 was around 1 mM, at pH 9. CMP
was conducted as a function of NaCl concentration added to the slurry. The MRR for both
substrates and calculated electrostatic force between abrasive particles from zeta potential values
are plotted in Figure 3-5.
The first thing to be monitored is the coagulation of particles whenever salt is added into
slurry. Figure 3-6 shows the particle size distribution as a function of NaCl concentration. Below
46
0.5 M NaCl, the particle size maintained a narrow mono size distribution. When the
concentration reached 0.5 M, gelation occurred and particle size distribution showed multiple
peaks. It is not clear from Figure 3-6 if there is coagulation, since the additional peak(s) from
coagulation are not noticeable due to the multiple peaks from gelation. It is very likely that there
is some degree of coagulation at that concentration. Gelation usually occurs at intermediate pH
and high salt concentration in a colloidal silica system, and it is different from coagulation.
Gelation is reversible, i.e. the dispersion stability can be restored simply by stirring or dilution,
but if the coagulation occurrs, it is not usually reversible. In gelation, silica particles form a
network by siloxane (Si-O-Si) bonds. In coagulation, they do not form any network, but they
simply collide with each other by Brownian motion leading to very strong attractive van der
Waals interactions. It is not known how gelation of abrasive particles affects the CMP
performance. A colloidal silica slurry adjusted to neutral pH and kept for some time to promote
geltation without any salt can be a good candidate to isolate such effects.
Below a salt concentration of 0.5 M, NaCl addition to the polishing slurry showed the
same trend in MRR change as the pH change. There was a steep decrease in the MRR after the
salt concentration exceeded the CCC (0.5 M NaCl) for silica, however. The silicon nitride
substrate did not show such dramatic change. It has been reported that at fixed solids loading, the
MRR decreases as a function of particle size after reaching a critical size of particles [62, 63].
This leads to the explanation of how the coagulation might affect MRR. At a fixed solids loading,
coagulation leads to two possible effects, (i) reduction in the number of abrasive particles
participating in the polishing process thereby decreasing the contact area between particles and
substrate, (ii) increased penetration depth due to size enlargement resulting in higher MRR. As
was discussed by Yeruva, optimal indentation depth is determined by the thickness of the
47
modified surface layer of silica caused by reaction with water, which is believed to be on the
order of nm in thickness [55]. Besides, the optimum mean particle size resulting in maximum
MRR was reported to be around 75 nm experimentally [63]. In the present study, the
agglomerated particle size is larger than 100 nm, hence a decrease in MRR and poor surface
finish are expected and experimental results confirmed these predictions.
Choi reported that at intermediate salt concentrations, Stöber silica slurry showed a broader
distribution with a larger particle size accompanying the MRR increase, and was attributed to
reduced electrostatic forces and increased particle size due to coagulation [64]. At a higher salt
concentration, they reported low MRR and high roughness values attributed to coagulation of the
silica abrasive particles. In the present study with a colloidal silica slurry, the increase in MRR
can solely be attributed to reduced electrostatic repulsion, since there was no particle size
increase.
Measured surface roughness values indicated that up to 0.3 M NaCl, there was not much
difference in surface roughness (Figure 3-7). However at 0.5 M, a rough surface with low MRR
on silica but not on silicon nitride was observed. On the silicon nitride substrate, the coagulation
of abrasive particles does not seem to have as high an effect as on the silica probably due to the
higher hardness of silicon nitride substrate as compared to the silica abrasive particles.
Salt addition has been reported to increase frictional force between the pad and substrate as
also observed by Mahajan [56]. This suggests that coagulation of abrasive particles is a major
factor in determining frictional forces, which in turn impact MRR.
In order to further establish a correlation between the MRR and electrostatic forces, the
MRR for both materials is plotted in Figure 3-8 as a function of electrostatic repulsive force
between colloidal silica abrasive particles at different levels of pH and salt concentrations.
48
Except under the extreme conditions such as pH 2, 11.5 and NaCl concentration of 10 mM,
where calculated electrostatic force was not sensitive to experimental variables, an inverse linear
relationship was observed between MRR and electrostatic forces.
Parameters Affecting Surface Finish in STI CMP
Figure 3-9 shows the surface roughness of silica and silicon nitride substrates as a function
of slurry pH. Selected surface morphologies and roughness profiles of the silica and silicon
nitride after CMP at pH 10.4 and 11.5 for both materials are plotted in Figure 3-10. CMP by
colloidal silica slurry improved the roughness of both materials below pH 11. 5 and silica
showed higher roughness values than silicon nitride over the entire pH range examined in this
study. At pH 11.5, CMP resulted in poor surface finish for both materials but the increase of
roughness was higher for silica. Scratches from the CMP process were not observed on either
substrate.
This variation of roughness follows exactly the same trend as the silica solubility results by
Iler [65]. It is known that the solubility of silica shows a steep increase in the basic pH condition.
Iler reported about a three orders of magnitude increase in silica dissolution rate as the pH value
changed from 2 to 11 [65]. The increase in solubility is believed to be due to the hydroxyl ion
(OH-) acting as a catalyst for attack by water on the siloxane (Si-O-Si) network. Specifically,
hydroxyl ions create an excess of electrons resulting in a higher negative surface potential and
consequently more attacks by H3O+ [21]. Therefore, it has been widely believed that the high
dissolution rate of silica at high pH is responsible for the high MRR [53, 54]. The effect of
solubility on surface roughness has not been well understood. It should be noted that solubility of
silica is known to depend on the curvature of the silica surface [66]. Hulett et al. reported that the
convex surface of colloidal silica shows higher solubility than the concave one, and a smaller
radius of curvature exhibits higher solubility [66]. This implies that surface convex impurities
49
will dissolve faster than flat substrates. However, this prediction is contrary to our experimental
observation of the effect of solubility on MRR and surface roughness, and requires further
investigation.
To evaluate the effect of solubility of silica and silicon nitride on CMP performance,
dissolution rate was determined by measuring the thickness of both substrates immersed in a
0.1M (pH 13) NaOH solution for 12 days without stirring (Figure 3-11). Surface roughness of
the substrates before and after dissolution is presented in Figure 3-12. The dissolution rate of
silica was three orders of magnitude higher than that of silicon nitride most probably due to
higher bond strength of the latter. Even though the experiment was conducted at pH 13, the
magnitude of dissolution of both substrates was relatively low. However, in a real CMP process,
dissolution can be increased by the imposed pressure resulting in higher tensile stress created by
the abrasive particles as they abrade silica surface. Nogami and co-workers reported a 50%
increase in solubility when 30 MPa compressive stress was applied compared with the stress-free
condition [47]. Additionally, when abrasive particles abrade the surface, the temperature can be
higher due to heat generated by friction. It has been reported by Iler that solubility of colloidal
silica increased by more than ten times at 200 oC than at room temperature [65]. However,
incorporation of all those factors still gives a far less dissolution rate than the MRR increase at
pH 11.4 for silica.
Regarding this apparent discrepancy, it should be noted that the attack of hydroxyl ions
will be higher at higher pH resulting in a softer layer, which can be removed easily, and is prone
to damage by abrasion. Consequently, the attack of hydroxyl ions increases the solubility and
promotes formation of a softer layer on the substrate at high pH. The dissolution of silica itself
does not seem to play a bigger role in determining MRR. The extent of hydroxyl ion attack will
50
also be dependent on the bond strength, and according to Young’s moduli of the materials, this
may explain the reason for low MRR and dissolution rate of silicon nitride.
Figure 3-12 illustrates the surface morphologies of the two substrates after dissolution at
pH 13 for 12 days. There was very small increase in surface roughness for both of the substrates.
The inverse pyramidal-type etch pits observed in Figure 3-13 are common phenomena when
highly concentrated alkaline solution is used for etching silicon in the micromachining of silicon
substrates [67-69].
The reason for the anisotropic etching is different reactivities of certain crystal planes of
silicon. In other words, anisotropic etchants etch much faster in one direction than in another,
which is usually (111) planes of silicon. Therefore, anisotropic etching of (100) silicon by
alkaline solution results in the inverse pyramidal-type etch pits, as was observed experimentally.
Since the thin film used in this research was deposited on (100) silicon, the silica film will have a
similar atomic arrangement as the underlying silicon. It has been well observed in silicon
anisotropic etching that when a dilute alkaline solution (20 wt%) is used, the etching produces
high surface roughness. Palik et al. reported that the high surface roughness is attributed to the
formation of hydrogen bubbles acting as a pseudomask, thus inhibiting uniform etching [69]. In
silica, the overall reaction of the dissolution can be described as follows:
≡ 9)-(3232 H21)OH(SiOHOHSiOH +=++ +−
Gas bubbles were observed during dissolution experiments and are believed to be hydrogen gas.
For the reaction shown in Equation (3-9) to occur, the nucleation of hydrogen bubbles is a
dominant step and it is much easier to nucleate them on high energy sites giving rise to surface
defects.
51
Salt Mediated Lubrication
Donose and co-workers have reported that various cations adsorbing on silica from
electrolyte solutions can induce lubrication through the formation of a hydrated cation layer [70].
Due to the difference in hydration enthalpy of different cations, resultant lubrication was
different for each added salt. Similar phenomena have been reported by Raviv and Klein by a
modified surface force apparatus [71]. They measured the shear force between mica surfaces and
concluded that hydration layers of adsorbed cations act as a highly efficient boundary lubricant.
Their research was mostly done by lateral force microscopy and the macroscopic effect on CMP
was not investigated.
Figure 3-14 shows the lateral force as a function of loading force in the presence of various
salts such as LiCl, NaCl and CsCl reported by Donose and co-workers [70]. According to their
results, every salt showed higher lubrication effect than pure water. The thickness of the
adsorbed cation layer increases with increasing electrolyte concentration. Highly hydrated
cations such as Li+ can form a thick and soft layer resulting in higher lubrication than poorly
hydrated cation such as Cs+. It was observed that the degree of lubrication followed their order of
hydration, which is Li+ > Na+ > Cs+. Schematics shown in Figure 3-15 illustrate this concept.
For pure water, at least one layer of water molecules are bound to the silica surface, but this layer
is relatively thin and firmly adsorbed to the silica surface resulting in rigid interface. In the
presence of an electrolyte solution, there is a thicker hydration layer than pure water. The model
suggested by Raviv et al. states that the cations surrounded with water molecules are very hard to
remove and remain fluid like in a lateral direction and promote lubrication. It is well known that
smaller Li+ ion has the highest hydration enthalpy and hydrated radius among various
52
cations[72]. Accordingly, Li+ ions have a thicker and more effective lubricating layer on silica
surface, while Cs+ ions have a thinner and less effective lubrication layer.
To investigate how the variations in lubrication affects the real CMP performance, CMP
was conducted as a function of applied polishing pressure using three slurries with no salt, 1 M
LiCl, and 1 M CsCl. The salt concentration was selected corresponding to the results reported by
Donose and co-workers [70]. The particle size was measured to assess if the selected salt
addition causes any coagulation of the abrasive particles (Figure 3-16). When appropriate
amounts of 5 M LiCl and 5 M CsCl were added to change the salt concentration, there was no
particle size increase initially up to about 10 minutes after mixing. As time passed, gelation took
place slowly and the peak height of the particle size decreased and the size distribution became
broader. While it is not well understood how the gelation affects the CMP performance, CMP
was performed 5 minutes after the mixing of 5 M salt solution to avoid the possible effect of
gelation and ensure uniform mixing of added salt. Surface roughness measurement showed that
RMS surface roughness was around 0.15 nm for all the conditions at the same polishing pressure
and there was no increase from salt addition. Therefore, it appears that the variation in MRR is
due to the effect of salt on material properties and not necessarily from gelation and coagulation.
Figure 3-17 shows the variation in the MRR with and without 1 M LiCl and CsCl as a
function of polishing pressure. Increase in the MRR with added salt suggests that electrostatic
interactions play a dominant role in polishing. The MRR of the silica substrate using a slurry
with 1 M LiCl is lower than that of 1 M CsCl showing results in agreement with those from
lateral force microscopy measurements. Considering the same electrolyte concentration in both
experiments, the electrostatic forces should be similar. The lubrication effect of individual
53
particles should reduce the MRR, but increase in the number of abrasive particles due to reduced
electrostatic repulsive forces between them seems to have resulted in overall higher MRR.
In summary, CMP performance using colloidal silica slurry in a silica and silicon nitride
system revealed that Young’s modulus of the substrate material is more likely the reason for the
differences in their MRR, with electrostatic repulsive force imposed by pH change in the slurry
playing a dominant role. The electrostatic interaction was validated by monovalent salt addition
to the slurry. A linear relationship between the MRR and electrostatic forces implied that such
repulsive interactions probably resulted in governing the number of particles engaged in the
polishing process. Dissolution rates were measured by immersing substrates into 0.1 M NaOH
solution for 12 days and the results showed that dissolution of silica was much higher than
silicon nitride, however, the rate of dissolution was too low to make any significant difference in
the MRR. It seems that the attack of hydroxyl ions at higher pH is responsible for poor surface
finish and higher MRR due to the formation of a softer top layer. Dissolution in alkaline
solutions produced a poor surface finish due to nucleation of hydrogen gas bubbles.
The effect of the nature of added ions on CMP performance was also investigated. The
Lubrication effect of hydrated cations was determined not to be a dominant factor in MRR.
However, a slurry with LiCl showed lower MRR than one with CsCl, which suggests that the
lubrication of the hydrated cations is playing a limited role in determining the MRR.
54
Table 3-1. Young’s modulus, hardness measured by nanoindentation method, material removal rate (MRR), ratio of MRR (CMP pressure of 7 psi), and ratio of Young’s modulus for silica and silicon nitride.
E (GPa) H (GPa) MRR (Å/min) MRRSiO2/MRRSi3N4 ESi3N4 /ESiO2 SiO2 84.6 ± 3.0 8.5 ± 0.3 4696 Si3N4 176.6 ± 2.5 23.5 ± 1.0 1382
3.4 2.1
55
Figure 3-1. Variations of mateiral removal rate (MRR) for silica and silicon nitride substrate as a function of applied pressure by using undiluted (30 wt%) colloidal silica slurry at pH 10.4.
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
0 2 4 6 8 10 12 14 16 18-1000
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
pH 10.4 SiO2 Si3N4
Pressure (psi)
56
Figure 3-2. Variations of MRR of silica and silicon nitride substrate and calculated electrostatic force between two abrasives as a function of pH of the diluted (12 wt%) colloidal silica-based slurry (Klebosol 1501-50).
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
2 4 6 8 10 12400
600
800
1000
1200
1400
1600
1800
2000
2200
2 3 4 5 6 7 8 9 10 11 12400
600
800
1000
1200
1400
1600
1800
2000
2200
2 4 6 8 10 12
0.00
0.02
0.04
0.06
0.08
0.10
MRR SiO2 MRR Si3N4
Ele
ctro
stat
ic F
orce
/R (m
N/m
)
Force
pH
57
Figure 3-3. Particle size distributions of colloidal silica slurry at two different pH conditions.
0.1 1-2
0
2
4
6
8
10
12
14
16D
iffer
entia
l Vol
ume
(%)
pH 10.4 pH 2
Particle Size (μm)
58
Figure 3-4. Zeta potential of colloidal silica slurry and electrostatic force between silica abrasive particles. Force was calculated from the zeta potential values by constant surface charge model. The distance between abrasives was assumed to be 1 nm.
2 4 6 8 10 12
-90
-75
-60
-45
-30
-15
0
1 2 3 4 5 6 7 8 9 10 11 12
0.00
0.02
0.04
0.06
0.08
0.10
Elec
trost
atic
For
ce/R
(mN
/m)
Zeta
pot
entia
l (m
V)
pH
59
Figure 3-5. Variations of MRR and calculated electrostatic force between two abrasives as a function of slurry NaCl salt concentrations in the slurry at pH 10.4.
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
101 102
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
101 1020
5
10
15
20
25
MRR SiO2 MRR Si3N4
NaCl Concentration (mM)
pH 10.4 Force
Ele
ctro
stat
ic F
orce
/R (m
N/m
)
60
Figure 3-6. Particle size distributions of colloidal silica slurry (Klebosol 1501-50, 12 wt%) as a function of salt concentrations at pH 10.4.
0.1 1
0
2
4
6
8
10
12
14
16
No Salt 0.1 M NaCl 0.3 M NaCl 0.5 M NaCl
Diff
eren
tial V
olum
e (%
)
Particle Size (μm)
61
Figure 3-7. Surface roughness of silica and silicon nitride substrate after CMP as a function of added salt (NaCl) concentration at pH 10.4.
0 0.3 0.50.0
0.1
0.2
0.3
0.4
0.5R
MS
Rou
ghne
ss (n
m)
NaCl Concentration (M)
SiO2 Si3N4
62
Figure 3-8. Material removal rate of silica and silicon nitride as a function of repulsive electrostatic force between silica abrasives: (a) pH effect and (b) Salt (NaCl) addition at pH 10.4.
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
300
600
900
1200
1500
1800
2100
2400
0.00 0.02 0.04 0.06 0.08 0.100.00 0.060.00 0.06
(a)
SiO2
Electrostatic force/R (mN/m)
Si3N4
0 5 10 15 20 25
600
900
1200
1500
1800
2100
2400pH 10.4(b)
()
E lectrostatic Force/R (mN/m)
SiO2 Si3N4
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
63
Figure 3-9. Surface roughness of silica and silicon nitride substrates after CMP as a function of slurry pH.
2 3 4 5 6 7 8 9 10 11 12 130.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
RM
S R
ough
ness
(nm
)
pH
SiO2 Si3N4
64
Figure 3-10. Surface morphologies and profiles of substrates from two pH conditions; (a) silica at pH 10.4, (b) silica at pH 11.5, (c) silicon nitride at pH 10.4, and (d) silicon nitride at pH 11.4
Si3N4, pH 11.5, RMS Roughness: 0.22 nm (d)
Si3N4, pH 10.4, RMS Roughness: 0.14 nm (c)
(a) SiO2, pH 10.4, RMS Roughness: 0.24 nm
SiO2, pH 11.5, RMS Roughness: 0.48 nm (b)
65
Figure 3-11. Material thickness change of silica and silicon nitride substrates as a function of immersed time in pH 13 NaOH solution. Ellipsometer was used to measure thickness change. Calculated dissolution rates were also shown.
Rem
oved
Thi
ckne
ss (Å
)
0 2 4 6 8 10 120
200
400
600
800
1000
1200
1400
1600
1800
2000
SiO2 Si3N4
Si3N4 Dissolution rate:
0.1 M NaOH (pH 13)SiO2 Dissolution rate:
Days
0.109 Å/min
0.966 x 10-3Å/min
66
Figure 3-12. Surface morphologies and profiles of substrates before and after etching in pH 13
NaOH solutions; (a) bare silica (b) silica after 12 days, (c) bare silicon nitride, and (d) silicon nitride after 12 days.
SiO2, RMS Roughness: 0.338 nm(a)
Si3N4, RMS: 0.204 nm, Rmax: 3.073 nm(c)
Si3N4 at pH 13 for 12 days, RMS Roughness: 0.241 nm (d)
(b)
Si3N4, RMS Roughness: 0.204 nm
SiO2 at pH 13 for 12 days, RMS Roughness: 0.434 nm
67
Figure 3-13. Etch pits formed on (a) silica and (b) silicon nitride substrate immersed in 0.1 M (pH 13) NaOH solution for 12 days.
(a)
(b)
68
Figure 3-14. Lateral force of a 6.8 μm silica particle interacting with a silica substrate in pure
water and CsCl, NaCl, and LiCl solutions of 1 M: dependence of friction on the applied load at a fixed scan rate of 2 μm/s [70].
69
Figure 3-15. Schematic representation of the hypothetical frictional mechanisms [70].
70
Figure 3-16. Particle size distributions of colloidal silica slurry (Fuso PL-7) without salt and with
1 M LiCl and 1 M CsCl.
0.1 1-2
0
2
4
6
8
10
12
14
16
18
PL-7 9.6 wt%, pH 7.3
No Salt 1 M CsCl 1 M LiCl
Particle Size (μm)
Diff
eren
tial V
olum
e (%
)
71
Figure 3-17. Material removal rate of silica substrates by CMP using diluted (9.6 wt%) colloidal silica slurries (PL-7) without salt and with 1 M LiCl and 1 M CsCl as a function of applied polishing pressure.
0 2 4 6 8 10 12
0
30
60
90
120
150
180
210
240
pH 7.3 Fuso PL-7 1 M CsCl 1 M LiCl No Salt
Mat
eria
l Rem
oval
Rat
e (n
m/m
in)
Pressure (psi)
72
CHAPTER 4 ROLE OF SURFACTANTS IN DEVLOPING SELECTIVE PASSIVATION LAYER IN CMP
In this chapter, a surfactant mediated passivation approach to increase STI CMP selectivity
is discussed.
Selective adsorption of a surfactant is necessary to develop selective passivation in CMP.
It can be achieved if there is an adequate difference in the surface charge characteristics of the
substrates. This concept has been successfully used to achieve selective coating of surfactants in
mineral flotation [73, 74]. Interactions between a solid surface and charged polar head of the
surfactant molecule determine the adsorption strength and the resultant adsorption density. In
CMP, surfactants have been used not only to disperse abrasive particles but also to create
lubricating layers, yielding passivation against polishing. In the present study, an anionic
surfactant, SDS, was used to create a selective passivating layer only on the silicon nitride and
not on the silica substrate. It is well known that isoelectric point (IEP) of silicon nitride is higher
than silica resulting in less negative potential for silicon nitride above the IEP [23]. The
concentration of SDS was adjusted to 16 mM, twice the critical micelle concentration (CMC),
which has been shown previously to yield stable dispersion of silica abrasives in a CMP slurry
[75].
For adsorption studies, silica from Geltech Co. and silicon nitride from Ube Co. (SN-E10)
were used to simulate silica and silicon nitride substrates. The particle size of silica was
measured to be around 0.53 µm by Coulter, and that of silicon nitride, which was measured by
centrifugal sedimentation was reported to be around 0.5 µm by the manufacturer. Their specific
surface areas were measured to be 8.1 m2/g and 10.4 m2/g, respectively by Quantachrome Nova
1200, BET surface area measurement technique. The specific surface area of abrasive silica
particles was measured to be 34 m2/g by Quantachrome Autosorb 1C-MS. The Phoenix 8000
73
UV-Persulfate TOC Analyzer was used to measure the SDS concentration. 99% sodium dodecyl
sulfate (SDS) surfactants from Acros Organics Co. and Fisher Scientific Co. were used as
received. 98% dodecyl alcohol from Eastman Kodak Co., 95% Sodium tetradecyl sulfate from
Acros Organics Co. and Tween 80 from Fischer Scientific were also used, as received.
High Selectivity Slurry Using Surfactants
The addition of SDS to the slurry was found to result in a lower value of MRR of silica and
silicon nitride in the entire pH range investigated in the present study (Figure 4-1). However,
significant increase in selective polishing of silica was measured below pH 3, yielding a
selectivity of 25 as compared to state-of-the-art ceria abrasives of 5. The silicon nitride surface
appeared to be fully passivated with the surfactant layer at a pH below its IEP of pH 4.5, with
minimal effect on silica CMP. The surface quality of substrates plotted in Figure 4-2 indicated
that surfactant addition did not cause any additional defects measured as root mean square
(RMS) roughness.
To understand the reasons for the observed selectivity, zeta potential and adsorption
density measurements were conducted as a function of slurry pH (Figure 4-3). The IEP of silicon
nitride and silica substrates were measured to be about pH 4.5 and pH 2.2, respectively. The
difference in the IEP results from the different surface groups constituting each material. As
mentioned earlier, acidic silanol (SiOH) are the major surface groups on silica, while the silicon
nitride surface consists of basic amine (Si2NH) and acidic silanol (SiOH) groups [23]. These
surface groups can acquire charge in aqueous solution according to following reactions:
1)-(4+− += HSiOSiOH
2)-(4++ =+ 222 NHSiHNHSi
74
Consequently, zeta potential of silicon nitride is more positive due to the positively charged
amine groups on its surface.
The adsorption density of SDS was measured to be higher on silicon nitride than silica at a
pH below their IEP. This is attributed to the resultant electrostatic interactions between the
substrate and surfactant molecules. At pH 2, the zeta potential of silicon nitride was measured to
be +40 mV, whereas, that of silica was around +3 mV. Accordingly, the adsorption density on
silicon nitride was determined to be six times higher than on silica resulting in complete
passivation of the former. At pH values above the IEPs for both materials, there was still
measurable adsorption on both materials, however, the adsorption density on silicon nitride was
higher probably due to more positive sites on silicon nitride from surface amine groups. There
have been several reports of SDS adsorption on the negatively charged silica surface. Hydrogen
bonding and sodium ion mediated surfactant bonding are proposed as plausible mechanisms [76,
77].
In order to measure the effect of surfactant concentration on selectivity, polishing was
conducted as a function of added surfactant concentration (Figure 4-4). The MRR for both silica
and silicon nitride started to decrease upon SDS addition and reached a minimum above 16mM.
The maximal decrease in the MRR for silica was around 20% from its original value, and that for
silicon nitride was more than 90%, resulting in 10 times higher polishing selectivity than without
surfactant addition. No further change in the MRR or selectivity was observed once the added
surfactant concentration exceeded 16mM. It has been reported that once the equilibrium
concentration reaches CMC, no more adsorption changes are observed due to electrostatic
repulsion between adsorbed micellar aggregates and free micelles in solution [78].
75
Surfactant Mediated Boundary Layer Lubrication for Selective Polishing
Vakarelski et al. have shown that beyond the CMC of the cationic surfactant
(dodecyltrimethylammonium bromide, C12TAB), there was no further decrease in the lateral
force on silica substrate [22]. Consequently, it is hypothesized that the maximum decrease in the
MRR will occur when the bulk concentration reaches the CMC of SDS (around 8mM) [79].
However, in the present study, two times higher concentration of surfactant than the CMC was
required to achieve maximum selectivity. The measurement of SDS adsorption on the CMP
slurry as a function of pH showed that about 91% of added (16mM) SDS adsorbed on the
abrasive particles at pH 2 as shown in Figure 4-5. The area per molecule using the Gibbs
adsorption equation, was calculated to be around 70 Å2/molecule, which is higher area per
molecule than the literature value of 53 Å2/molecule [79] at the liquid/gas interface.
The possible reasons for the higher dosage of surfactant than expected are that the
surfactant adsorption does not reach true equilibrium conditions due to process conditions
encountered in CMP. This phenomenon may also be related to the dynamic aspects of surfactant.
The reported τ2 for SDS is around 2.32 × 10-3 s [80]. However, according to Patist and co
workers, when 15 mM SDS was used for foaming experiments, the dynamic surface tension
decreased as a function of bubble life time until it reached the saturation after about two seconds
[81]. Recently, Philipossian et al. have reported the mean residence time of colloidal silica slurry
between pad and substrate to be of the same order of a few seconds under the present
experimental conditions [82]. Assuming that other conditions are similar, the mean residence
time in our study is expected to be 2 - 3 seconds. Considering that these two numbers are
comparable, migration of surfactant to the newly formed substrate surface may be limited due to
the high speed rotation of pad and wafer in CMP.
76
The adsorption free energy is the driving force for surfactant adsorption and is the sum of
various molecular interactions [78]. In the current study, it can be categorized into two categories,
(i) interactions between the polar head of SDS and the surface through electrostatic and hydrogen
bonding, and (ii) hydrophobic interactions between alkyl chains of adsorbed SDS molecules. By
using the measured adsorption density plotted in Figure 4-5, and the radius of the SDS micelle
(20 Å) [79], the adsorption free energy of SDS on silica abrasives was calculated to be -3.58
kcal/mol at pH 2 using modified Stern-Graham equation [78].
3)-(4⎟⎟⎠
⎞⎜⎜⎝
⎛=
kTΔG
-exprC2Γoads
o
where, Γ is the adsorption density, r is the effective radius of the adsorbed ion, k is the Boltzman
constant, Co is the bulk concentration, T is 298 K, and oadsGΔ is the adsorption free energy. The
electrostatic component of the adsorption free energy was calculated to be -0.76 kcal/mol using
zeψδ [78], where, z is the valency of the adsorbate species, e is the charge of the electron, and the
ψδ is the potential at the δ plane (assumed to be the zeta potential). These calculations indicate
that significant adsorption of the surfactant on the abrasive particles is more favorable and may
act as an additional energy barrier.
Optimization of High Selectivity Slurry
It is clear from the above discussion that the adsorption density of surfactant molecules on
the substrate is an important factor in determining the slurry selectivity. In order to reduce the
required dosage of the surfactant, longer alkyl chain length surfactants were examined, since it
was expected to exhibit better lubrication effects at a lower amount of added concentration. This
is attributed to the formation of more compact surfactant layers [75]. The MRR and polishing
selectivity as a function of alkyl chain length of the sodium alkyl surfactant are plotted in Figure
77
4-6. The surfactant concentration was selected to be twice the CMC value to compensate for the
loss of surfactant due to adsorption on silica abrasive particles. As expected, SDS with longer
alkyl chain length (C12) resulted in higher MRR decrease for silicon nitride with almost
negligible effect on silica, thus yielding higher selectivity than sodium decyl sulfate (C10).
However, when sodium cetyl sulfate (C14) was examined, there was a smaller decrease in MRR
of silicon nitride resulting in lower selectivity. Considering that the Krafft point of C14 sodim
sulfate (30 oC) [80] is higher than room temperature and higher than that of SDS (16 oC) [80],
the surfactant was not completely solubilized and therefore failed to form a functional
passivation layer.
Another approach to decrease the dosage of the surfactant required to achieve desired
selectivity involved using mixed surfactant system (Tween 80/SDS and dodecyl alcohol/SDS) at
pH 2. The MRR and selectivity for the selected systems are plotted in Figure 4-7. In the case of
dodecanol and SDS, selectivity was lower for the mixed surfactant system than for 16mM SDS
alone. It is possible that the addition of a small amount of dodecanol promotes adsorption of SDS
both on silica and silicon nitride. Although there was no appreciable change in the MRR on
silicon nitride, the higher adsorption of SDS on silica also passivated its surface.
It has been reported by Pala and co-workers that surfactant mixture of SDS and various
nonionic surfactants can produce synergistic effects for dispersion of slurry under high ionic
strength conditions [34, 37]. When 8mM Tween 80 was added to 16mM SDS, the MRR of silica
was highly suppressed, whereas that of silicon nitride remained almost unchanged, thereby
resulting in poor selectivity. It is well known that nonionic surfactant such as Tween 80, which
has ethylene oxide groups (OC2H4), can adsorb on silanol groups (SiOH) on silica through
hydrogen bonding [83]. These observations strongly suggest that a surfactant or surfactant
78
system that exhibits strong preference only for silicon nitride is essential for developing
surfactant-based high selectivity slurries.
In summary, colloidal silica, which shows high dispersion stability in the range of pH 2 to
11, was utilized to develop a high selectivity slurry. The addition of SDS at pH 2 resulted in
more than ten times higher selectivity than the conventional slurry. Additionally, AFM
roughness measurement showed an acceptable surface finish. Adsorption density measurements
revealed that there is a preferential higher adsorption of SDS on silicon nitride, possibly due to
electrostatic attraction, as compared to silica. The SDS adsorption results in differential
passivation/lubrication and hence lower polishing efficiency of silicon nitride as compared to
silica. The CMP characteristics examined as a function of added SDS showed that decrease in
MRR and increase in selectivity leveled off at about twice the surfactant CMC and remained
unchanged, thereafter. The surfactant requirements appear to be driven by their adsorption
primarily on silica abrasive particles. To reduce the surfactant dosage, longer alkyl chain length
surfactants were tested, which yielded higher selectivity at lower dosage. However, the addition
of a long chain length alcohol to substitute for the surfactant resulted in lower selectivity,
probably due to higher adsorption of the surfactant on silica. Mixed ionic and nonionic surfactant
systems, on the other hand, resulted in poor selectivity due to passivation of both silica and
silicon nitride, although to a different degree.
79
Figure 4-1. Influence of SDS addition on CMP performances: (a) Variation of material removal rate (MRR) as a function of slurry pH with and without 16mM sodium dodecyl sulfate (SDS), (b) Accompanying selectivity of the slurry.
1 2 3 4 5 6 7 8 9 10 110
5
10
15
20
25
30
2 4 6 8 101 2 3 4 5 6 7 8 9 10 11
Selectivity without SDS(b)
Sel
ectiv
ity
pH
Selectivity with SDS
-400
0
400
800
1200
1600
2000
2400
2800
3200
1 2 3 4 5 6 7 8 9 10 11pH
MRR with SDS SiO2 Si3N4
MRR Without SDS SiO2 Si3N4
(a)
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
80
Figure 4-2. Surface finish of silica and silicon nitride substrates processed with standard and high
selectivity slurry.
Standard (pH 10.4) pH 2 16mM SDS at pH20.00
0.07
0.14
0.21
0.28
0.35
Rou
ghne
ss (R
MS
, nm
) SiO2 Si3N4
81
Figure 4-3. Variation of zeta potential of silica and silicon nitride substrate and adsorption
density of 16mM SDS on silica and silicon nitride powder measured by total organic carbon (TOC).
1 2 3 4 5 6 7 8 9 10 11-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
2 4 6 8 100.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
SiO2 Si3N4
Zeta
Pot
entia
l (m
V)
Ads
orpt
ion
dens
ity (μ
mol
/m2 )
pH
SiO2 Si3N4
82
Figure 4-4. Variation of MRR and accompanying selectivity of Klebosol slurry (12 wt%) as a function of added SDS concentration at pH 2.
-3 0 3 6 9 12 15 18 21 24 270
70
140
210
280
900
1200
1500
1800
2100
2400
2700
-3 0 3 6 9 12 15 18 21 24 270
10
20
30
40
50
Sel
ectiv
ity
MRR SiO2 MRR Si3N4
()
SDS Concentration (mM)
pH 2
Selectivity
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
83
Figure 4-5. Adsorption density of SDS on 12 wt% Klebosol slurry with 16 mM SDS as a function of pH.
2 4 6 8 10 12
2.20
2.22
2.24
2.26
2.28
2.30
2.32
2.34
2.36
2.38
pH
Ads
orpt
ion
dens
ity (μ
mol
/m2 )
84
Figure 4-6. Effect of alkyl chain length of sodium alkyl sulfate on MRR and selectivity at pH 2. The concentration was adjusted to 2 times the CMC to compensate the loss during CMP process.
C10 C12 C140
30
602000
2100
2200
2300
2400
2500 MRR SiO2 MRR Si3N4 Selectivity
C10 C12 C140
10
20
30
40
50pH 2
Sel
ectiv
ity
Mat
eria
l Rem
oval
Rat
e (A
/min
)
66 mM C10
Sodium Sulfate
16 mM C12
Sodium Sulfate
4.2 mM C14
Sodium Sulfate
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
85
Figure 4-7. MRR and selectivity obtained by slurries with various surfactant and surfactant mixtures at pH 2. Slurry A (16mM SDS) was included for comparison purpose.
0
30
60
1000
1500
2000
2500
0
10
20
30
40
50pH 2
Sel
ectiv
ity
MRR SiO2 MRR Si3N4 Selectivity
Mat
eria
l Rem
oval
Rat
e (A
/min
) A
B
C
A = 16 mM SDS B = 0.8 mM Dodecanol/15.2 mM SDS C = 8 mM Tween 80/16 mM SDS
Mat
eria
l Rem
oval
Rat
e (Å
/min
)
86
CHAPTER 5 ADSORPTION STUDY OF SODIUM DODECYL SULFATE ON SILICA
Considering the relevance of surfactants in developing selective CMP slurries, it is
important to understand their adsorption mechanisms on different substrates to optimize their
performance as passivating agents. Accordingly, adsorption behavior of sodium dodecyl sulfate
(SDS) on silica was studied. Special emphasis was placed on SDS adsorption on colloidal silica
particles at high pH where both constituents exhibit negative charges.
To measure adsorption density of SDS on colloidal silica particles, diluted Klebosol
colloidal silica slurry (12 wt%) was prepared. After dilution, the slurry pH was measured to be
around 10.4. Suspension pH was adjusted with HCl and KOH solutions prepared with analytical
grade substances purchased from Fisher Scientific Co. A proper amount of 100 mM SDS
solution was added to obtain 1 to 5 mM SDS concentrations. Higher SDS concentrations were
achieved by adding dry SDS powder. Adsorption density measurement incorporated the
following steps: 1) add surfactant to silica suspension, 2) magnetically stirring for 10 min, 3)
centrifuge at 1500 rpm and 4) separate appropriate amount of supernatant and dilute with
nanopure water to yield a concentration within calibration range (around 50 ppm). Finally, 40
ml vials were loaded to total organic carbon (TOC) analyzer and measure the residual (bulk)
SDS concentration in the supernatant. Concentration of adsorbed surfactant on particle was
calculated from the difference in input concentration and residual bulk concentration. Specific
surface area of colloidal silica was measured to be 34 m2/g by Quantachrome Autosorb 1C-MS.
In order to gain insight into specific binding mechanisms, Fourier transform infrared
spectroscopy (FTIR) measurements were conducted. A nitrogen-purged Nicolet Magna 760
spectrometer equipped with a DTGS detector was used to conduct FTIR analysis.
FTIR/attenuated total reflection (ATR) method is well established for its sensitivity to the
87
surface property change [84-86]. Since it is well known that the surface of silicon is covered with
silica by spontaneous oxidation, Si ATR crystal and surfactant solution were used to investigate
the adsorption behavior. All the spectra were the results of 512 co-added scans at a resolution of
4 cm-1. Surfactant solutions of different concentration of SDS were prepared at pH 10.4. During
the measurement, the solutions were added to the Si ATR crystal assembly. After adding
surfactant solution the sample chamber was purged with dry N2 gas to remove any residual
atmospheric moisture and CO2. After 20 minutes of purging, CO2 peaks disappeared, however
H2O peaks could not be eliminated.
Adsorption Behavior of SDS on Silica
Adsorption isotherm of SDS on colloidal silica suspension (Klebosol 1501-50, 12 wt%)
measured at pH 10.4 is given in Figure 5-1. Despite the fact that both SDS surfactant and silica
surface are negatively charged at this pH, the isotherm appears to be similar to that of
electrostatic interaction dominant adsorption behavior [87-89]. In region I, where adsorption
density is not high, adsorption is assumed to occur by electrostatic attraction. In region II, a
sudden increase in adsorption is attributed to hemimicelle formation. In region III, there is a
decrease in the rate of adsorption as indicated by change in the slope, which is ascribed to bilayer
formation. Adsorption in region IV reaches a constant value apparently due to micelle adsorption
on the surface [90].
In the current study, at low equilibrium surfactant concentrations up to 1.6 mM, the
adsorption is very small due to electrostatic repulsion between SDS and silica substrate.
However, there was a measurable adsorption prrobably due to hydrogen bonding. Beyond 1.6
mM, the adsorption increases sharply and may be attributed to attractive hydrophobic
interactions between alkyl chains of surfactant resulting in hemimicelle formation. Beyond 8
mM, adsorption density leveled off. Critical hemimicelle concentration (HMC) and critical
88
micelle concentration (CMC), which can be inferred from the adsorption isotherm in Figure 5-1
occurred at around 1.6 mM and 8 mM, respectively. In the previously reported SDS-alumina
system with a background electrolyte of 0.1 M NaCl, HMC and CMC were reported to be around
0.05 mM and 1.6 mM, respectively [91]. In the current system, electrostatic repulsion plays a
dominant role in controlling adsorption behavior at lower surfactant concentration, thereby
resulting in relatively higher HMC on silica. Beyond HMC, hydrophobic attractive forces govern
surfactant adsorption process.
Under saturation adsorption conditions, the average area per molecule was calculated to be
41.6 Å2 from adsorption isotherm, which compares favorably to 53 Å2 reported at the air-water
interface for SDS [80]. In the case of SDS-alumina system, it was calculated to be around 23.7
Å2 indicating the formation of more compact surfactant aggregates due to attractive electrostatic
interactions between SDS and alumina. The area covered by the adsorbed SDS molecules on
silica particles was calculated to be 663.2 m2, assuming the area occupied by one SDS molecule
to be 53 Å2. Considering that the total area of silica particles is 520.2 m2, surface coverage by
SDS molecules indicates the formation of a bilayer, if this surface is assumed to be homogenous,
or micellar type adsorption, otherwise. In the latter case, using the reported aggregation number
(64) and the radius of SDS micelle, 20 Å [79, 92], it was calculated that there are total 1.95
× 1019 micelles adsorbed onto silica particles. Therefore, in the steady state, 47.2% of the silica
surfaces is covered with SDS micelles. Using a theoretical density of 2 g/cm3, 15.3 g of silica
particles in the slurry, and the particle radius of 45nm, the number of silica particles in 100 ml
slurry was calculated to be 2 × 1016. This value indicates that approximately 9.75 × 102 SDS
micelles coat each particle.
89
To further understand the adsorption of SDS on similarly charged silica, adsorption energy
was calculated using modified Stern-Graham equation (Equation (4-3)). Calculated adsorption
free energy under saturation adsorption conditions was found to be -2.9 kcal/mol indicating that
primarily physical adsorption is responsible for SDS adsorption on silica.
In order to assess the effect of pH on SDS adsorption on silica, measurements were
conducted at 1.6 mM and 16mM concentrations. Results plotted in Figure 5-2 show the
adsorption behavior of SDS on silica correlates well the zeta potential of silica indicating that
surface charge of the silica plays an important role in SDS adsorption. Adsorption energy
calculations revealed that at 1.6 mM SDS concentration, adsorption energy at pH 10.4 is 0.02
kcal/mol as compared to -1.17 kcal/mol at pH 2, indicating an energetically unfavorable process
at pH 10.4 and a favorable one at pH 2. At 16mM, the electrostatic effect was probably
dominated by the increased hydrophobic attractive interactions between alkyl chains resulting in
adsorption energy of -3.14 kcal/mol at pH 10.4 and -3.59 kcal/mol at pH 2 indicating favorable
adsorption at both pH values.
Structure of Adsorbed SDS Molecules
To investigate the structure of adsorbed SDS molecules on silica surface, zeta potential
was measured as a function of added SDS concentration at pH 10.4 (Figure 5-3). At very low
concentration of SDS (region I), zeta potential essentially remains unchanged. As the
concentration increases (region II), sodium ions are adsorbing on the silica surface resulting in
less negative zeta potential. It is hypothesized that there are surfactant molecules weakly bonded
to sodium ions. Surfactant molecules associated with sodium ion will not exhibit significant
impact on the zeta potential measurements due to mutual charge neutralization. As the surfactant
concentration increases, hemimicelles form and grow in size in region III, and the slope of zeta
potential increase becomes smaller than region II. It seems that free SDS starts to adsorb on the
90
hemimicell coated surface forming bilayers in region III resulting in a lower slope change.
Finally in region IV, when surfactant aggregates form micelles, zeta potential reversal occurs by
incorporating a number of free monomers in the solution. When a background electrolyte was
added to the system, overall zeta potential was less negative and the slope change was less
pronounced, but a similar trend was observed. Above hypothesis is shown schematically in
Figure 5-4.
The correlation between SDS adsorption and zeta potential is clearer from Figure 5-5,
where adsorption density and zeta potential are co-plotted as a function of equilibrium
concentration of SDS. The change in zeta potential follows the adsorption isotherm and zeta
potential reversal occurs at CMC. However, due to the low surface coverage of micelles (4%) on
silica surfaces the change was not significant.
SDS adsorption behavior on silica, as determined in the present study, is contrary to
electrostatic considerations, since both the substrate and surfactant molecules are similarly
charged. There have been several reports of SDS adsorption on negatively charged silica or
sepiolite, a hydrated magnesium silicate (Si12Mg9O30(OH)6(OH2)4H2O) [76, 77, 93]. Possible
mechanisms for this observation were hydrogen bonding between silanol groups and SDS, and
counter ion mediated surfactant adsorption. Several noticeable thermodynamic properties of the
surfactant were reported by Özdemir et al. through the adsorption study of SDS on sepiolite. At
saturation adsorption, the adsorption free energy calculated from Frumkin model was -3.1
kcal/mol at 25 oC [93]. It is comparable to the results in the present study (-2.9 kcal/mol). This
low energy of adsorption indicates that weak physical forces are responsible for adsorption.
Calculated adsorption free energy from the adsorption isotherm in Chandar’s report was -4.18
91
kcal/mol, indicating that the driving force for adsorption involving electrostatic attraction is
higher than hydrogen bonding alone.
To further investigate the mechanism of SDS adsorption on negatively charged silica
particles, FTIR ATR (attenuated total reflection) measurements were conducted. It should be
mentioned that quantitative analysis by FTIR is not very accurate and it is not well understood
how the electrostatic interaction affects the FTIR spectra. On the other hand, adsorption of
various molecules via hydrogen bonding has been well observed and documented [94, 95]. One
of the noticeable research on hydrogen bonding behavior for silica and dibenzodioxin was done
by Guan et al. [95]. They reported that as the adsorption of dibenzodioxin on silica surface
increased, the peak of geminal silanol group decreased and that of isolated silanol group
increased, indicating that the molecular adsorption occurs at the expense of the silanol group by
hydrogen bonding. Their measurement was done using dry powder samples. In the current study,
all the measurements were conducted in aqueous surfactant solution by using ATR crystal.
Figure 5-6 shows the spectra of SDS at 1, 2.5, 5 and 10 mM concentration in the CH2
stretching region measured at pH 10.4. As was discussed by Pankaj et al., the absorbance
intensity increased up to 5mM, and it decreased at 10mM, which is higher than CMC of SDS (8
mM) [96]. The reason for the decrease of absorbance intensity upon micelle formation is not well
understood, however, it confirms the adsorption of SDS on silica surface at high pH. Due to the
overlapping of the peaks from silanol groups and water, changes in silanol groups were not
confirmed in this experiment.
SDS adsorption on silica can also impact the dispersion stability. Figure 5-7 shows the
particle size distribution of Stöber silica without and with SDS 12 hours after the pH was
changed to 2. Without SDS, there was an additional peak due to particle coagulation since the
92
isoelectric point (IEP) of the silica particle is known to be around pH 2.7. However, with SDS
addition, no additional peaks were observed.
In summary, SDS adsorption behavior at low concentration was small due to electrostatic
repulsion, however, limited adsorption was observed due to hydrogen bonding. At intermediate
concentrations, it was hypothesized that sodium ion mediated charge neutralization along with
hydrophobic attractive force resulted in higher adsorption of SDS. The slope of the adsorption
density decrease is attributed to bilayer formation. Adsorption free energy calculations and zeta
potential measurements as a function of SDS concentration were supportive of the proposed
hypothesis. It was observed that SDS adsorption on silica surface resulted in a stable dispersion.
93
Figure 5-1. Adsorption isotherm of SDS on colloidal silica (Klebosol 1501-50, 12 wt%) at pH 10.4. Critical hemimicelle concentration (HMC) and critical micelle concentration (CMC) was marked.
100 101 102
10-8
10-7
10-6
pH 10.4
HMC
IVIII
II
I
CMC
Adso
rptio
n de
nsity
(mol
/m2 )
Bulk Equilibrium SDS Concentration (mM)
94
Figure 5-2. Adsorption density of SDS on colloidal silica (12 wt% Klebosol 1501-50) at SDS concentration of 1.6 mM and 16 mM and zeta potential as a function of pH.
2 4 6 8 100.0
2.0x10-8
4.0x10-8
2.0x10-6
2.2x10-6
2.4x10-6
2.6x10-6
2 4 6 8 10-100
-80
-60
-40
-20
0
Zeta
pot
entia
l (m
V)
Ads
orpt
ion
Den
sity
(mol
/m2 )
pH
1.6 mM SDS 16 mM SDS
Zeta potential
95
Figure 5-3. Zeta potential of Klebosol slurry as a function of SDS concentration at pH 10.4.
-74
-73
-72
-71
-70
-69
-68
-67
-66
-65
100 101 102
Klebosol 12 wt%
no Salt
Zeta
pot
entia
l (m
V)
Concentration of SDS (mM)
pH 10.4
IV
III
II
I
1 mM NaCl
96
Figure 5-4. Pictorial depictions of the possible surfactant aggregates films at concentrations corresponding to I-IV in Figure 5-3.
Na Na Na Na Na Na
SSiiOO22
Na Na Na Na Na Na Na Na Na
SSiiOO22
Na Na Na Na Na Na Na Na Na Na Na Na Na
SSiiOO22
Na Na Na Na Na Na Na Na Na Na Na Na Na
SSiiOO22 Na
I
II
IV
III
97
Figure 5-5. Adsorption characteristics of SDS on Klebosol silica slurry and zeta potential as a function of concentration of SDS at pH 10.4.
100 101
10-8
10-7
10-6
-72.5
-72.0
-71.5
-71.0
-70.5
-70.0
-69.5
-69.0
HMC
CMC
Ads
orpt
ion
Den
sity
(mol
/m2 )
Bulk Equilibrium SDS Concentration (mM)
Adsorption density
pH 10.4 IVIII
II
I
Zeta potential
98
Figure 5-6. FTIR/ATR Spectra of SDS solution at 1, 2.5, 5 and 10 mM bulk concentration in the CH2 stretching region (2921, 2924) measured at pH 10.4 using Si ATR crystal.
3100 3000 2900 2800 2700
Wavenumber (cm-1)
0.000
0.002
0.004
0.006
0.008
0.010
0.012 pH 10.4SDS
1mM 2.5mM 5mM 10mM
log
(1/R
)
99
Figure 5-7. Particle size distribution of Geltech SiO2 at pH 2 with and without 16 mM SDS 12 hours after pH change.
0.5 1.0 1.5 2.0 2.5 3.0
0
5
10
15
20
25
30pH 2
D
iffer
entia
l vol
ume
(%)
Particle size (μm)
without SDS with SDS
100
CHAPTER 6 APPLICATION OF DENSITY FUNTIONAL THEORY BASED MODELING FOR
SURFACTANT ADSORPTION STUDY
There have been numerous modeling efforts to develop reliable tools for predicting
colloidal systems behavior. There are two broad areas of modeling for investigating the structure
of molecules and their reactivity: molecular mechanics and electronic structure theory. They
perform the same basic calculations: i) compute the energy of a particular molecular structure
and ii) geometry optimization to produce the lowest energy molecular structure [97]. In addition,
electronic structure model is capable of calculating vibrational frequencies of molecules resulting
from interatomic motion.
Molecular mechanics based models use the laws of classical physics, and each one is
characterized by its particular force field. In general, it does not explicitly treat the electrons in a
molecule. They perform computations based on the interactions among the nuclei, while
interactions involving electrons are implicitly included in force fields through parameterization.
This approximation enables the molecular mechanics modeling to be fast and cost effective, and
applicable to large systems. However, it also has several limitations, e.g., each force field is
system specific, and it is unable to calculate chemical problems where electronic effects
predominate (i.e. bond formation and breakage), since interactions among electrons are neglected
[97].
Electronic structure methods use the laws of quantum mechanics. There are two major
classes in the area, i) semi-empirical methods such as AM1 and PM3, which utilize parameters
derived from experimental data, ii) ab initio methods, which utilize no experimental parameters,
instead, computations are based solely on the laws of quantum mechanics and the values of
several physical constants. Semi-empirical calculations are relatively inexpensive and produce
101
reasonable qualitative descriptions, while ab initio modeling can provide high quality
quantitative predictions for a broad range of systems [97].
Recently, newly developed electronic structure methods termed density functional theory
(DFT) methods, have been widely used. DFT methods are attractive since they include the
effects of electron correlation, while pure ab initio methods take it into account in an average
sense. In general, electronic structure methods are known to require high computational time and
are relatively costly. Many efforts to model colloidal systems use molecular mechanics methods,
since the system involves relatively large molecules or molecules/particles in water, which is too
large to be calculated by electronic structure model [98, 99]. However, electronic structure
methods are also applied to colloidal systems in many cases due to their ability to produce FTIR
and Raman spectra and that they can be used without parmeterization-a must have for molecular
mechanics modeling [100-102].
In this study, DFT method was applied to theoretically calculate adsorption and compare
it with the experimental data.
Methodologies
DFT methods compute electron correlation via general functionals of the electron density.
DFT functionals partition the electronic energy into several components which are computed
separately: the kinetic energy, the electron-nuclear interaction, the Coulomb repulsion, and an
exchange-correlation term accounting for the remainder of the electron-electron interactions.
Various DFT methods are distinguished by the way that they treat the exchange and correlation
term. In addition, there are several hybrid functionals, which combines the component of ab
initio method and DFT methods. Among them the best of these hybrid functionals is Becke-style
3-Parameter Density Functional Theory using the Lee-Yang-Parr correlation functional (B3LYP).
This method has proven to be superior to the traditional functionals in terms of computational
102
cost and accuracy of predicting experimental results. Due to these advantages, B3LYP method
was utilized throughout the current study [97].
Another important component of the theoretical calculation by electronic structure
method is basis set. Basis set is the mathematical description of the orbitals within a system used
to perform the theoretical calculation. Standard basis sets for electronic structure calculations use
linear combinations of Gaussian functions to form orbitals. Basis sets assign a group of basis
functions to each atom within a molecule to approximate its orbitals. There are several basis sets
[97]:
• Minimal basis sets (STO-3G): they contain the minimum number of basis functions needed for each atom. They use fixed size atomic-type orbitals and three Gaussian primitives per basis function.
• Split valence basis sets (3-21G and 6-31G): they increase the number of basis functions per atom. Split basis sets have two or more sizes of basis function for each valence orbital. They allow orbitals to change size, but not to change shape.
• Polarized basis sets (6-31G* (6-31G(d)) and 6-31G** (6-31G(d,p))): they allow orbitals to change shape. They add d functions to carbon atoms and f functions to transition metals. 6-31G(d) basis set, which adds d function to heavy atoms is becoming very common for calculations involving up to medium-sized systems. Another popular basis set is 6-31G(d,p). It adds p functions to hydrogen atoms in addition to the d functions on heavy atoms.
In the current study, medium sized 6-31G* basis set was utilized for both optimization, single
point energy calculation, and frequency calculation in the current study, considering calculation
time.
The most unique and useful advantage of the electronic structure methods over molecular
mechanics model is its ability to do frequency calculation. It can serve a number of different
purposes, such as to predict Raman and IR spectra of molecules and to produce thermodynamic
properties such as free energy, enthalpy and entropy of the system. Energy calculations and
geometry optimizations discussed so far ignore the vibrations in molecular system, which is not
103
true of real systems. In geometrically optimized states, these vibrations are regular and
predictable, and molecules can be identified by their characteristic spectra. Specifically, it can
predict the direction and magnitude of the nuclear displacement that occurs when a system
absorbs a quantum of energy. In addition, all frequency calculations include thermodochemical
analysis of the system. By default, it is carried out at 298.15 K and 1 atmosphere of pressure.
Gaussian ’03, which is used in the present study provides thermal correction for enthalpy and
free energy through frequency calculation. Using the sums of electronic and thermal enthalpies
and electronic and thermal free energies, enthalpies and free energies of reactions can be
calculated for a model system as described below [103].
1)-(6DCBA +=+
2)-(6))H()H(()H()H(
)H()H()K298(H
BcorroAcorroDcorroCcorro
tstanreaccorroproductscorroo
r
+++−+++=
+−+= ∑∑εεεε
εεΔ
3)-(6))G()G(()G()G(
)G()G()K298(G
BcorroAcorroDcorroCcorro
tstanreaccorroproductscorroo
r
+++−+++=
+−+= ∑∑εεεε
εεΔ
where, εo is the total electronic energy, Hcorr and Gcorr are thermal correction of enthalpy and
free energy, respectively produced through frequency calculations. The above method works
since the number of atoms of each element is the same on both sides of the reaction, hence all the
atomic interactions cancel out, requiring only molecular data for calculation. Adsorption free
energy presented in the current study was calculated using the above methods.
The above calculations are carried out in vacuum. The properties of molecules and
transition states can differ considerably between the gas phase and in solution. For example,
electrostatic effects are much less important for species placed in a solvent with a high dielectric
constant than they are in the gas phase. There are methods developed to incorporate the solvent
effects. All models consider the solvent as a continuum of uniform dielectric constant ε and the
104
uniform reaction field. The solute is placed into a cavity within the solvent. The effect of
polarization of the solvent continuum is determined numerically [97].
The optimization of each molecule and complex structure was done using 6-31G* B3LYP
self-consistent field (SCF) computation. For complex structures, adsorbent and adsorbate were
placed close to each other, around 5 Å initially. After optimization, according to the interactions
between two molecules, the bond length and angle between them was adjusted. Single point
energy and adsorption free energy calculations were done after structure optimization in vacuum
state. Adsorption (electronic) energy of the complex is calculated by the following equation:
4)-(6)EE(EE ASSAads +−=
where, ESA is the energy of the complex structure of adsorbate-adsorbent pair, and Es and EA are
the energies of the constituent molecules. Frequency calculation was conducted to calculate
adsorption free energy. Single point energy calculation by polarized continuum model (PCM)
was performed on optimized model system in vacuum to calculate the energy in aqueous
environment.
Structures and Resources
The basic molecular structures used in this research are shown in Figure 6-1. To model
silica and silicon nitride surface, SiO4 and SiN4 tetrahedral units were used, which are the
minimal building blocks of silica and silicon nitride. There are several reasons for selecting these
structures, firstly simple structure can save time and computational resources, secondly, as the
structures become complicated, optimization with 6-31G* basis set was not always possible. The
importances of the frequency calculation are discussed in detail in a later section. The
optimization of the structure is the prerequisite for frequency calculation. It was one of the most
important considerations for the current study. Additionally, when a complicated structure (i.e. 1-
105
dimensionally bonded five tetrahedrons of silica) was used, deformation of original structure
occurred as a result of optimization providing multiple bonding sites with other molecules, which
is not possible in a real system. Even though there are multiple bonding sites, calculated
adsorption energy values had the same order of magnitude. Finally, materials for the
experimental study are amorphous, which have only short range order individual tetrahedron is
bonded to each other randomly. Therefore, actual repeat unit of silica and silicon nitride can be
considered as individual tetrahedron.
The basic structure used in this study consists of one Si and four oxygens for silica and one
Si and four nitrogens for silicon nitride. All oxygens and nitrogens are protonated to represent
the condition at isoelectric point (IEP). To simulate different pH values, individual tetrahedron
was deprotonated or protonated to be positively or negatively charged.
Zhmud et al. reported similar approach using ab initio, Hartree-Fock (HF) method on
clusters of β-Si3N4 and β-cristobalite structures and nitrogen gas as an adsorbate [102].
Although they were able to successfully correlate the experimental results and theoretical
prediction, their adsorption energy values were all positive due to non-equilibrium structures.
There also have been reports using simple structure similar to the current study [104, 105] that
were successful in prediction of FTIR spectra with small deviations from experimental results,
and can be considered to be in support of our approach using minimal sized structures.
Sodium dodecyl sulfate (SDS) structure was used without any modification. Triton X-100
(TX-100, C14H22O(C2H4O)n, n ~ 10) structure was simplified to n = 1 to reduce calculation time.
All the computations for the current study were done by Gaussian ’03 on a node of 4 AMD
Opteron Cores (2 x 275, 2.2GHz) with 4 GB DDR400 RAM in Unix system. Computational time
106
varied according to the complexity of the system. Usually, it took several hours for optimization
and frequency calculation for complex structure of surface and surfactants.
Results and Discussion
After structure optimization, the bond length of Si-O in Si(OH)4 and Si-N in Si(NH2)4 was
measured to be around 1.660 Å and 1.746 Å, respectively. Experimentally determined values for
the bonds were reported to be 1.6041 ~ 1.6066 Å and 1.704 ~ 1.767 Å for quartz and β-silicon
nitride, respectively [106, 107]. It appears that despite the use of minimal structure, the
optimized bond length values are comparable to the crystallographic data. This may be attributed
to the high symmetry structures used in the current study.
Electronic energies for different complex molecular structures were calculated in vacuum,
in water were using polarizable continuum model (PCM), and the Gibbs adsorption free energies
were calculated with the method described before. These data are presented in Table 6-1, and the
experimental values are listed in Table 6-2. Due to the lack of data for Triton X-100 (TX-100),
adsorption free energy was calculated from the data reported by Denoyel and co-workers [108].
In further discussion, sodium dodecyl sulfate (SDS) surfactant will be denoted as Na+ and
DS- to describe the dissociated state in aqueous solution. Negative energy values indicate that
interaction (adsorption) between two molecular species is energetically favorable and vice versa.
Calculated adsorption free energy, which considers electronic vibrational motion at room
temperature, showed values similar to the electronic energy calculated in vacuum at O oK
implying that the contributions from the motion and elevated temperature are very small. In
contrast, PCM correction resulted in significant effect on the energy values. As mentioned in the
methodologies section, polarizable water medium reduced the energy significantly providing
data more comparable to the experimental results.
107
SDS Adsorption on Silica at, below, and above the Isoelectric Point (IEP)
Initially, Si(OH)4 at isoelectric point (IEP) of silica and in presence of surfactant (DS-) was
simulated. After structure optimization, DS- adsorbed on neutral Si(OH)4, forming a bond
between one of the oxygens in surfactant and a hydrogen on silica. It was found that a relatively
weak bond (Eads(PCM) = -8.46 kcal/mol) is responsible for the adsorption and equilibrium bond
length was 1.881 Å (Figure 6-2). The low energy values or weak interactions indicate that
electrostatic force or hydrogen bonding is responsible for adsorption. However, considering that
the surface neutrality, it may be attributed to the hydrogen bonding. When compared to the
experimental results, PCM energy calculation resulted in the same order of magnitude but much
higher value. This could be due to an intrinsic error when electronic structure method deals with
low energy interactions.
Below IEP of silica, when SiO4H5+ interacts with DS-, electrostatic attraction as well as
hydrogen bonding is plausible (Figure 6-3). The additional hydrogen is more tightly bonded to
the DS- than the surface. The bond length was calculated to be about 1.001 Å. The bond length
of hydrogen bonding was calculated to be 1.895 Å, which is similar to the neutral surface. Due to
these two interactions, the calculated PCM energy was much higher (-20.97 kcal/mol) than the
neutral surface at IEP. However, experimentally the difference was not noticeable, since the
measured zeta potential had a negative value even at pH 2. It is likely that in a real system,
SiO4H5+ is difficult to exist and neutral Si(OH)4 (considered to be neutral silanol group) is the
major surface species at low pH.
Above IEP of silica, when SiO4H3- interacts with DS-, structure optimization was not
possible since both of the surfaces and DS- molecules kept diverging away from each other to
lower the energy of the system (Figure 6-4). This must be caused by the electrostatic repulsion
between them. Although the energy is not calculated from optimized structure, the positive
108
energy value calculated in vacuum indicates that the adsorption is not favorable in this case. Due
to the non-equilibrium structure, PCM energy showed slightly negative value, however the final
geometry clearly indicates that the adsorption is not favorable. Calculated energy from the
experimental data was also positive (above IEP at SDS concentration of 1.6 mM at pH 10.4) and
is similar to the theoretical prediction.
The interaction behavior dramatically changed when Na+ ion was introduced into the
system (Figure 6-5). Sodium ion resided between two species resulting in an equilibrium
complex structure. The two molecules formed tighter bond than hydrogen boding and the energy
(-22.05 kcal/mol) was similar to the value from attractive electrostatic interactions below IEP.
Sodium ion was found to be more tightly bonded to the surface. The bond length between Na+
and O- on surface was around 2.184 Å and that of Na+ and O- in DS- was around 2.34 ~ 2.36 Å.
SDS Adsorption on Silicon Nitride at IEP
Current theoretical approach was attempted to predict selective adsorption of surfactant
onto a specific surface. For this purpose, silicon nitride tetrahedron structure was constructed and
protonized for charge neutrality (Figure 6-6). In the case of silicon nitride surface, optimization
of ionized structure was not possible probably due to instability of the structures. Therefore,
comparison between with neutral silicon nitride and silica at IEP is attempted.
At IEP of silicon nitride, when Si(NH2)4 interacts with DS-, hydrogen bonding occurred
with a bond length of 2.158 Å. Calculated PCM energy was determined slightly lower (-8.29
kcal/mol) than with Si(OH)4. This trend is not consistent with the experimental results, where
silicon nitride showed higher adsorption density at pH 2. Regarding this discrepancy, it should be
noted that the current modeling is to simulate the interactions of surfactant and the representative
of the major surface reactive sites under given conditions. In reality, each surface has a different
charge density due to different number of active sites constituting the surface.
109
TX-100 Adsorption on Silica at IEP
To verify the applicability of the current approach to another surfactant, adsorption of TX-
100, a nonionic surfactant, onto neutral Si(OH)4 was investigated (Figure 6-7). It is well known
that TX-100 adsorbs onto a silica surface at different pH levels via hydrogen bonding [108].
After structure optimization, they were determined to form equilibrium complex structure. Low
values of calculated PCM energy indicate formation of hydrogen bonding on the same order of
magnitude as the experimental results. The bond length between oxygen in SiOH4 to hydrogen in
TX-100 and oxygen in TX-100 to hydrogen in SiOH4 was measured to be 1.9 Å and 1.849 Å,
respectively, similar to the value of DS- and SiOH4.
It is known that the electronic structure method is not capable of accurately describing low
energy interactions [101, 109]. Consequently, calculated energy values from the DFT method are
usually overestimated [109]. In addition, calculated intermolecular interactions are also
overestimated due to the basis set superimposition error (BSSE) [102]. These intrinsic errors
seem to also exist in the current study. Various approaches have been attempted to reduce the
calculation errors. Volkov et al. reported that electrostatic energy can be successfully calculated
with minimum error when DFT B3LYP was used with higher order basis sets. To reduce BSSE,
counterpoise (CP) correction, which calculates each of the units using just the basis functions of
the other, has been reported to be successful [102]. To refine our current modeling study, these
elements needs to be incorporated to give more realistic energy values. The ideal setting would
be frozen surfaces that do not change their geometry after optimization, with multiple surface
sites, using B3LYP and a higher order basis set than 6-31G*, with CP correction for calculating
complex structures. However, this may significantly increase computational costs. Overall, the
current relatively simple setting has been shown to describe the given system with reasonable
success.
110
In summary, electronic structure method B3LYP with 6-31G* basis set was applied to
describe the interaction of molecules with different surfaces. Among various energy values
calculated, PCM energy, which takes into account the presence of solvent (water), provided the
most realistic results. It was found that adsorption of DS- onto neutral silica (at IEP) and silicon
nitride occurs via hydrogen bonding, and the positively charged silica surface and DS- (below
IEP) resulted in a stronger adsorption energy due to electrostatic attraction. The adsorption of
DS- onto negatively charged silica surface (above IEP) was not energetically favorable. However,
the introduction of Na+ greatly facilitated these interactions yielding similar adsorption energy
values as the electrostatic attraction. Adsorption of DS- onto the neutral silicon nitride surface
was also attempted. It was found that adsorption is energetically favorable via hydrogen bonding,
however, calculated values did not successfully correlate to the selectivity of surfactant
adsorption measured experimentally. To verify the validity of the current approach, adsorption
behavior of another surfactant, TX-100, onto neutral silica was also investigated. It was
determined that the adsorption was favorable via hydrogen bonding and the theoretical
predictions agree well with the experimental results reported for this system. Computational
optimization of the structures in the presence of solvent, and the interactions of surfactant with
the resultant surface systems, is required to more realistically simulate systems of practical
significance.
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Table 6-1. Adsorption energy (kcal/mol) calculated by density functional theory (DFT) based
method (B3LYP) using 6-31G* basis set. All the values were obtained after optimization with the same basis set. Eads (Vacuum) is the electronic energy calculated in vacuum, Eads (PCM) is the electronic energy calculated with polarizable continuum model (PCM) correction, which considers the effect of water solvent, and ΔGads (Vacuum) is the adsorption free energy calculated in vacuum.
SiO2 Si3N4
pH Condition at IEP below IEP above IEP at IEP at IEP
Complex Si(OH)4 /DS-
SiO4H5+
/DS- SiO4H3
-
/DS- SiO4H3
-
/Na+/DS- Si(OH)4 /TX-100 Si(NH2)4
/DS-
Eads (Vacuum) -30.05 -142.73 4.87 -63.61 -12.72 -16.76
Eads (PCM) -8.46 -20.97 -0.56 -22.05 -8.20 -8.29
ΔGads (Vacuum) -17.15 -128.44 N/A -52.55 -0.91 -5.95
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Table 6-2. Adsorption free energy (kcal/mol) of SDS on silica calculated from adsorption density
data in Ch. 5 at different pH and two different added concentrations (1.6mM and 16mM). The energy of Triton X-100 (TX-100) on silica at pH 2.2 calculated from data in Ref. [108].
SDS TX-100 ΔGads (kcal/mol)
pH 2 pH 3 pH 10.4 pH 2.2
1.6 mM -1.17 -1.16 0.018
16 mM -3.59 -3.59 -3.14 -2.79
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Figure 6-1. Optimized (a) Si(OH)4, (b) Si(NH2)4, (c) Sodiumdodecyl sulfate (SDS), and (d) Triton X-100 (TX-100) structure using B3LYP method and 6-31G* basis set.
(a) (b)
(c)
(d)
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Figure 6-2. Optimized SiOH4 and DS- complex structure using B3LYP method and 6-31G* basis set.
115
Figure 6-3. Optimized SiOH5+ and DS- complex structure using B3LYP method and 6-31G*
basis set.
116
Figure 6-4. Sturcture of SiO4H3- and DS- complex. Optimization is not complete, since two
molecules are being separated to decrease energy.
117
Figure 6-5. Optimized SiO4H3-, Na+, and DS- complex structure using B3LYP method and 6-
31G* basis set.
118
Figure 6-6. Optimized Si(NH2)4 and DS- complex structure using B3LYP method and 6-31G* basis set.
119
Figure 6-7. Optimized SiOH4 and TX-100 complex structure using B3LYP method and 6-31G* basis set.
120
CHAPTER 7 CONCLUSIONS AND SUGGESTIONS FOR FUTURE WORK
Conclusions
Colloidal silica, exhibiting high dispersion stability in the pH range of 2 to 11, was utilized
to develop a high selectivity slurry. The addition of SDS at pH 2 resulted in more than ten times
higher selectivity than conventional slurry. Additionally, AFM roughness measurements
indicated acceptable surface finish. Adsorption density measurements revealed that there is
preferentially higher adsorption of SDS on silicon nitride possibly due to more favorable
electrostatic interactons as compared to silica. SDS adsorption behavior is believed to result in
differential passivation (lubrication) and hence, lower polishing efficiency of silicon nitride as
compared to silica. The CMP characteristics examined as a function of added SDS concentration
showed that the decrease in MRR and increase in selectivity leveled off at about twice the
surfactant CMC and remained unchanged thereafter. Surfactant requirement appears to be driven
primarily by adsorption on silica abrasive particles. It appears that selective surfactant
passivating coatings on substrates can yield higher selectivity without any adverse impact on
surface finish. Attempts to economize on surfactant amount revealed that longer alkyl chain
length surfactants yielded higher selectivity at a lower dosage. However, the addition of long
chain length alcohols as a substitute for the surfactant resulted in lower selectivity possibly due
to passivation of silica. Mixed ionic and nonionic surfactant systems resulted in poor selectivity
due to larger decrease in MRR of silica and smaller decrease for silicon nitride. These findings
can be used as a guide for developing selective polishing CMP slurry.
In order to find an alternative method to achieve high selectivity, the effect of different salt
addition on CMP performance was investigated. Passivation (lubrication) effect by hydrated
cations was found not to be a dominant factor in determining MRR. However, slurry with LiCl
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yielded lower MRR than one with CsCl, suggesting that passivation by hydrated cations is
dependent on the nature of the added ions.
Attempts to study the role of materials properties in STI CMP using a colloidal silica slurry
indicated that differences in Young’s modulus of the substrate materials play an important role in
MRR since modulus is related to the bond strength of the materials.
Electrostatic repulsive forces imposed by pH change in the slurry plays a dominant role in
determining MRR of silica and silicon nitride substrates. These interactions were also
manipulated by monovalent salt addition to the slurry. A linear relationship between MRR and
electrostatic forces implies that such repulsive interactions probably resulted in governing the
number of particles engaged in the polishing process.
In order to assess the effect of dissolution in CMP, dissolution studies were conducted by
measuring thickness changes of substrates immersed in 0.1 M NaOH solution. The dissolution
rate of silica was found to be much higher than that of silicon nitride. However, it was too low to
make any significant contribution to MRR. It seems that the attack of hydroxyl ions at high pH
levels is responsible for the high MRR and poor surface finish due to formation of a softer top
layer.
SDS adsorption behavior on silica was investigated systematically. At low concentration,
SDS adsorption was opposed by electrostatic repulsion, however, some adsorption did occur and
was attributed to the hydrogen bonding. At intermediate concentrations, it was hypothesized that
sodium ions mediated charge neutralization along with hydrophobic attractive forces resulted in
higher adsorption density. Adsorption free energy calculations and zeta potential measurements
as a function of SDS concentration seem to support the proposed mechanism. It was observed
122
that SDS adsorption on silica surface, even at a pH below its IEP, resulted in stable dispersion.
FTIR/ATR measurements confirmed the adsorption of SDS on silica substrate.
The electronic structure modeling method B3LYP with the 6-31G* basis set was applied to
assess the interaction of molecules with different surfaces. Among various energy values
calculated, PCM energy, which takes into account the presence of solvent (water), provided the
most relevant results. It was found that adsorption of DS- onto neutral silica (at IEP) and silicon
nitride occured via hydrogen bonding and interaction between positively charged silica surface
and DS- (below IEP) resulted in higher adsorption energy due to electrostatic attraction. The
adsorption of DS- onto negatively charged silica surface (above IEP) was not energetically
favorable. However, the introduction of Na+ greatly facilitated these interactions yielding
adsorption energy values similar to those of the electrostatic attraction. Adsorption of DS- onto
the neutral silicon nitride surface was also attempted. It was found that adsorption is
energetically favorable via hydrogen bonding, however, calculated values did not successfully
correlate with the selectivity of surfactant adsorption as measured experimentally. To validate
the current modeling approach, adsorption behavior of another surfactant, TX-100 (nonionic),
onto neutral silica was investigated. It was determined that the adsorption occurred via hydrogen
bonding and theoretical predictions agreed with the experimental results for this system.
Additional efforts such as computational optimization of structures in the presence of solvent and
interactions of surfactant with the resultant surface systems are needed to simulate practical
systems.
Suggestions for Future Work
Frictional force measurements using lateral force microscopy are needed to investigate and
optimize the formation of surfactant mediated selective passivation coatings. The effectiveness
of the high selectivity slurry developed in the current study has been limited to chemical
123
mechanical polishing around pH 2. Development of robust high selectivity slurry for CMP at
higher pH levels is needed for practical applications. It has been well documented that alkali
metal introduced to semiconductor device can be a source of device failure. The high selectivity
slurry developed in the present investigation involves sodium ions, which can be a reason for
device failure. To avoid this possibility, application of ammonium based surfactants is suggested.
Besides surfactants, there are a number of other potential passivating agents such as
polymers, surface complexing agents, and proteins that exhibit preferential adsorption on
specific substrates. These materials may be good candidates for developing high selectivity
slurry for applications over a wide pH range. In addition, applications of high selectivity slurry
can be extended to other CMP system such as copper, low k materials (i.e. porous silica, Cu-
doped silica, etc.) for high speed devices, and high k materials (Hf based metal) for sub 45 nm
devices. When surfactants and polymers are used to develop passivations layers, it needs to be
ensured that there are no residual molecules left after the cleaning process. Application of a
passivating agent concept to high selectivity abrasives, e. g., ceria, is recommended. In this
application, since ceria is highly positively charged under the pH conditions suggested,
surfactant may preferentially coat the abrasive particles.
Quantitative investigation of the number of abrasive particles participating in the polishing
process is also recommended for reliable prediction of MRR.
With regard to modeling efforts, a systematic study using different methods, basis sets, and
other corrections, such as CP method, is recommended to refine current modeling approach for
colloidal systems. Increase in the size of molecules is recommended to accommodate cluster
effects. A method that can deal with flat surfaces with reactive atoms is recommended. In the
proposed method, optimization of structure occurs only on the surface and not under the surface
124
in a way to maintain the surface structure. To develop a predictive tool for selective adsorption
of surfactant, molecular mechanics based modeling is recommended, since it can incorporate
effects of individual water molecules and zeta potential of the surface. Molecular mechanics has
advantages for systems involving weak interactions (physical adsorption) and the electronic
structure method has advantages for strong interactions from chemical adsorption. Therefore, a
combination of both modeling methods is recommended to develop guidelines for formulating
selectively polishing CMP slurries.
125
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BIOGRAPHICAL SKETCH
Kyoung-Ho Bu was born on April 7, 1970 in South Korea. He graduated from Kyungnam
high school in Busan, Korea. He received his B.S. and M.S. degrees in inorganic materials
science and engineering from Seoul National University, Seoul, Korea in 1994 and 1996,
respectively. From 1996 to 1998, he was a research engineer in the Electronic Materials Division
of the Institute for Advanced Engineering (IAE), where he performed research in the fabrication
and characterization of Pb(ZrxTi1-x)O3 thin film micro-mirror arrays for projection display
applications. From 1999 to 2002, he was assistant manager in the plasma display panel (PDP)
R&D center of Orion Electric Co., Korea. His research interests were cell design and gas
discharge physics for high efficient plasma display.
In fall 2002, he started his Ph.D. study with Professor Brij Moudgil at the University of
Florida where he has worked at the Engineering Research Center for Particle Science and
Technology. His dissertation research concentrated on selective chemical mechanical polishing.
He graduated from the University of Florida with a doctorate degree in materials science and
engineering with electronic materials and particle science and technology specialties in May
2007.