Atomistic process modeling based on Kinetic Monte Carlo and Molecular Dynamics for optimization of advanced devices

L. Pelaz1, L. Marques1, M. Aboy1, P. Lopez1, I. Santos1, R. Duffy2

1University of Valladolid, Valladolid, Spain. Tel: +34 983 185502, Fax: +34 983 423675, Email: lourdes@ele.uva.es 2Tyndall National Institute, Cork, Ireland

Abstract Combined Molecular Dynamics and Kinetic Monte Carlo simulations are used in hierarchical models to gain physical understanding for process optimization in advanced devices. Thermal budget for the removal of defects in advanced millisecond anneals is evaluated. Alternatives to overcome the imperfect regrowth of narrow Si structures are proposed. The compromise between implant and anneal parameters for doping of FinFETs are presented, considering lateral diffusion and activation.

Introduction

The complexity of the physics involved in the fabrication of advanced devices, the dimensional (3D) nature of their architecture with nanoscale feature sizes, and increasing computer power promote the use of atomistic process modeling to conquer the challenges associated to their processing. Ion implantation remains the main candidate to achieve highly active dopant concentration in a controlled way. Even though it is a well established technique, new issues that arise in the fabrication of advanced devices demand improved models. Analysis of angle and energy distribution of backscattered ions or damage generated by heavy molecular implants, require detailed models. Dynamic annealing during implant needs also to be taken into account to accurately predict damage and dopant profiles and to optimize devices. In addition, the introduction of alternative processing techniques, such as plasma doping, or new materials, such as Ge, open a number of challenges to develop predictive process modeling. In this work we present classical Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC) simulation schemes to cover relevant process issues for advanced devices and gain physical insight for process optimization.

MD and KMC process models

MD relies on the use interatomic potentials to describe the dynamics of the system. This implies choosing an adequate mathematical expression and a subsequent fitting process to experimental results y ab-initio calculations. Several formulations and parametrizations for the Si-Si interaction have been extensively tested (1,2) and this technique has been successfully used to study intrinsic defects in Si (1-3).

However, its use has been much more limited for the description of the kinetics of dopants in Si (4) or for other semiconductors, such as Ge (5), because of the lack of appropriate potentials. This technique is computationally very expensive because the typical time step to solve the classical Newton equations of an atomic system is in the order of the fs. Although large cells with several thousand atoms can be simulated with parallel computing, the simulated time scale is still limited to ns or μs for a reasonable computer time. Nevertheless, by running simulations at very high temperature, the dynamics can be accelerated. Thus, MD is not only used to define parameters and mechanisms for simplified modes, but also as a direct simulation approach. Non-lattice KMC methods ignore Si lattice atoms and focus on defects and dopants events. The time step is variable and significantly longer than MD since the vibrational atomic movement is not considered. Thus, KMC can reach actual device sizes and time scales and it has been used to simulate junction formation in Si devices (6,7). Nevertheles, this method require a huge number of parameters to define the probability associated to the different events that dopant and defects can perform.

Hierarchical simulation schemes MD and KMC methods cover a time and space scale relevant for processing of advanced devices, as indicated in Fig.1. They are a good bridge between ab-initio calculations and empirical continuum models and represent a good compromise between computer efficiency, accuracy and predictability.

Fig. 1: Typical time and space scales of MD and KMC simulations. Simulated time (s)

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MD simulations provide a detailed description of the damage generated by molecular implants, as shown in Fig. 2a. MD is also used to understand the mechanisms involved in damage generation (8) and to set the basis for simplified models (9) based on the Binary Collision Approximation (BCA) by accounting not only on the traditional ballistic damage generation, but also due to the local melting of the region where enough energy has been deposited, as shown in Fig. 2b.

MD has been used to characterize the structural properties and energetics of the bond defect and amorphous regions with different geometries (Fig. 3a). This defect set the basis of KMC model for amorphization (10) in which activation energy for its recombination increases with the number of neighboring atoms not located in crystalline positions, as shown schematically in Fig. 3b.

Process optimization based on MD-KMC models

Dynamic anneal during implant can be appropriately engineered to optimize the thickness of the amorphous phase and minimize residual damage. This requires detailed description of the damage morphology (provided by MD or improved BCA models) combined with KMC models to reach macroscopic time scales. For a given ion energy and fluence (dopant profile) wafer temperature (Fig. 4a) and flux (Fig. 4b) can be chosen to define the thickness of the amorphous region.

The thermal budget required to remove residual defects depends on the defect concentration and the proximity of residual defects to interfaces that act as sinks. Millisecond anneals may not be able to completely remove stable extended defects (Fig. 5a). Flexibly Shaped Pulse Flash Lamp Annealing (FSP-FLA) extends the thermal budget during several milliseconds at lower temperature than the peak of FLA (11) and allows for additional defect removal (Fig.5b).

In Fig. 6 we give a 3D schematic of a FinFET. The whole fin width can be easily amorphized when implanting dopants to create source and drain extensions (12). It has been showed that imperfect regrowth of narrow FinFETs oriented along occurs due to the formation of {111} boundaries that start at the interfaces and merge around the middle of the fin (13). This is a serious concern because of the degradation of device performance and variability (12-14). In this paper we illustrate through simulations specific alternative processes to overcome this problem.

(a) (b) Fig. 2: (a) MD simulation of damage generated by the impact of B18H22 ion in Si. Red lines are the B trajectories. (b) BCA simulation of damage generated by 18 simultaneous B cascades. Si interstitials and vacancies are from the classical ballistic model. Additional bond defects are produced by local melting (improved model)

(a) (b) Fig. 3 (a) MD simulation cell including crystalline Si atoms and amorphous regions. The dynamics of the system indicates that dilute damage recrystallizes faster. (b) Simplified KMC scheme. Only defects are considered. The activation energy for recombination (E) is assumed to increase with the number of non-crystalline neighboring atoms. (EA

For FinFETs oriented along a solution is to produce partial amorphization, leaving a crystalline seed in the middle or in one of the sides of the fin (if only one side is implanted (15)). In this way, regrowth occurs sideways along . Only the regrowth of the fully amorphized top of the fin is limited by planes, as illustrated in Fig. 7. MD simulations indicate that even if the center of the fin is highly damaged (but not fully amorphized) the lateral regrowth is good (Fig. 8).

Fig. 7. MD simulation of the regrowth of a Fin oriented along with a perfect crystalline seed in the center. Only planes formed at the top prevent complete regrowth.

Fig. 8. MD simulation of the regrowth of a section of a FinFET with two lateral amorphous regions and a highly damaged but not fully amorphized region in the center, which is able to drive proper regrowth.

A crystalline seed can be achieved by choosing appropriate implant parameters. Fig. 9 corresponds to KMC simulations of Arsenics implant at slightly elevated temperature to prevent full amorphization of the FinFET. If the wafer temperature is increased further, the amorphous region thickness is reduced but more defects remain after regrowth. Proper regrowth can also be enhanced even if the fin width is fully amorphized in FinFETs aligned along . In this case, the crystalline region under the gate can trigger regrowth, as shown in Fig. 10. Residual defects may remain at the a/c interface under the gate after regrowth. Doping of FinFETs is particularly challenging because of the difficulties to introduce dopants in the sidewalls (13). The optimization of dopant profiles in fin devices implies a careful choice of tilt angles (as close to 45º as possible but avoiding shadowing), implant energy (to introduce dopants into the sidewalls but without losing ions through the opposite side of the fin) and fluence (to compensate for inefficient dopant incorporation).

Detailed experimental 3D characterization of dopant profiles is very challenging, especially for very narrow fins. Atomistic simulation can provide a useful insight. Fig 11 and 12 show the cross sectional and lateral views respectively of the as-implanted B distribution for FinFETs under different implant conditions. The poor dopant incorporation along the fin for low tilt angles can be improved with higher energy or higher dose implants. These implants also introduce more dopants under the gate.

Fig. 9. Cross sectional view of the simulated damage in a partially amorphized FinFET implanted with Arsenic (a) as-implanted at slightly elevated temperature. (b) Residual damage after regrowth. (c) Residual damage when amorphization during implant has been completely prevented by raising the wafer temperature.

Fig. 10. MD simulation of the regrowth of a fully amorphized FinFET oriented along with a crystalline seed under the gate.

Fig. 11. Cross sectional view of the B concentration in a FinFET implanted with (a) 0.5 keV B 1015 cm-2, 10º tilt, (b) 2 keV B 1015 cm-2, 10º tilt, (c) 0,5 keV B 5x1015 cm-2, 10º tilt, (d) 0,5 keV B 1015 cm-2, 45º tilt.

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Thermal budgets required for defect removal and dopant activation enhance the non-conformal dopant distribution of highly tilted implants (Fig. 12). 45º implants followed by millisecond anneals produce conformal profiles with good activation and minimal diffusion under the gate (Fig 13).

Fig 13. Lateral view of the electrically active B concentration in a FinFET implanted with 0.5 keV B 1015 cm-2, 45º tilt and annealed (a) 1050ºC spike (b) 1350ºC peak temperature flash (c) FSP-FLA (1250ºC peak temperature +10 ms recovery time).

Conclusions

MD and KMC simulation tools have been used to develop physically based hierarchical models and to directly address challenging issues in advanced device processing. Detailed models for ion implanted damage and amorphization have been illustrated as well as 3D evaluation of defect removal and dopant diffusion required in device architectures such as FinFETs.

Acknowledgments

This work has been partially funded by the Spanish Government under project TEC2008-06069, and by Junta de Casilla y Leon under project VA011A09.

References

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Fig. 12. Lateral view of the B concentration in a FinFET implanted with the same conditions as in Fig. 11, as-implanted (left) and after 1100ºC spike anneal (right).

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