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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 1
Chapter 9Chapter 9
Design for Manufacturability and YieldDesign for Manufacturability and Yield
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 2
What is this chapter about?What is this chapter about?
� Introduce Design for Manufacturability (DFM) and related concepts
� Focus on:
� Design for Yield (DFY) and yield models
� Photolithography
� Relationship between DFM, DFY, DFT
� Design for Excellence (DFX)
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 3
Design for Manufacturability & YieldDesign for Manufacturability & Yield
� Introduction
� Yield
� Components of Yield
� Lithography
� DFM and DFY
� Variability
� Metrics for DFX
� Concluding remarks
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IntroductionIntroduction
� In the 1960s all aspects of IC design and manufacture could be confined to a single organization
� Through the 1980s specialized fabs were common for individual products
� Throughout this time technology followed Moore’s law, with ever increasing integration
� Increased integration came with increasing costs
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DisaggregationDisaggregation
� Rising manufacturing costs led to ecosystem with specialized fabs fed by fabless semiconductor companies
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 6
YieldYield
� Difficulty lies in separating good from bad
� Competing definitions of “good”� Ideal: works in customer’s application
– Can’t measure this until it’s too late!
� Example ambiguity: high leakage from defects and/or fast transistors
� Practical definition “passes the tests we apply”
chipstotal
chipsgoodYield =
partsall
rejectsfalseescapestestpartsgoodYieldMeasured
−+=
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Components of YieldComponents of Yield
� Systemic problems (foundry)
� Related to processing, managed by
foundry
� Kinds: spatial across wafer, layer-specific,
machine-specific,etc.
� Example: Oxide problems due to
deposition
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Components of YieldComponents of Yield
� Systemic problems (foundry)
� Parametric variations (foundry, IP vendor)� Designs characterized over parametric window
(“slow/typ/fast”)
� Foundry process must remain within this window
� Example parameters:
– Channel length/width variation
– nMOS to pMOS length ratio variation
– Effective gate oxide thickness variation
– Doping variation - threshold voltage and diffusion resistance
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 9
Components of YieldComponents of Yield
� Systemic problems (foundry)
� Parametric variations (foundry, IP vendor)
� Defect-related yield (foundry, IP vendor, design team)
� Susceptibility to shorts and opens
� Caused by particles, cracks, voids,
scratches, missing vias, etc.
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 10
Components of YieldComponents of Yield
� Systemic problems (foundry)
� Parametric variations (foundry, IP vendor)
� Defect-related yield (foundry, IP vendor, design team)
� Design-related yield (IP vendor, design team)� Increasingly important issue
� Examples: optical, physical/mechanical, electrical
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Components of YieldComponents of Yield
� Systemic problems (foundry)
� Parametric variations (foundry, IP vendor)
� Defect-related yield (foundry, IP vendor, design team)
� Design-related yield (IP vendor, design team)
� Test-related yield (test vendor)� Examples: overkill from guardbanding, test escapes
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Components of YieldComponents of Yield
� Systemic problems (foundry)
� Parametric variations (foundry, IP vendor)
� Defect-related yield (foundry, IP vendor, design team)
� Design-related yield (IP vendor, design team)
� Test-related yield (test vendor)
� DFY concerned with all of these
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Yield modelsYield models
Yield = systematic yield
* parametric yield
* defect yield
* design yield
* test yieldAD
S eYY−⋅=
Poisson
α
α
+
=AD
YY S
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Negative Binomial
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=1
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Bose-Einstein
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−=
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eYY
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Murphy
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Origins of ModelsOrigins of Models
� Poisson Model
� Defects are events from a random process with a uniform defect density
� Murphy Model
� Defect density is not uniform, approximate Gaussian with a triangle distribution
� Bose-Einstein Model
� Some layers more susceptible than others to defects, density uniform per layer
� Negative Binomial Model
� Defects tend to cluster together
AD
S eYY−⋅=
Poisson
α
α
+
=AD
YY S
1
1
Negative Binomial
( )nSAD
YY+
=1
1
Bose-Einstein
2
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−=
−
AD
eYY
AD
S
Murphy
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Yield and RepairYield and Repair
� Four classes of yield:
� Systematic problems (foundry)
– Mild cases can be helped by repair
� Parametric variations (foundry, IP vendor)
– Mainly covered by design margin
– Local extreme cases (e.g. weak cells) can be helped by repair
� Defect-related yield (foundry, IP vendor, design team)
– Most can be fixed with repair
� Design-related yield (IP vendor, design team)
– Covered by improved design practice
� Repair mainly addresses #3 (defects) at 90nm and
above
� At 65nm and below, variability also important
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System-on-Chip Test Architectures Ch. 9 - Design for Manufacturability and Yield - P. 16
Repairable MemoryRepairable Memory
� Memory fails more than logic
� Repairable memory improves yield
( )ADerepairP
yieldbaserepairPY
−−⋅=
−⋅=∆
1)(
)_1()(
( )( ) nADnADDAA
nn
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eerepairPe
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r −−+−−−⋅+=
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Potential Yield ImprovementPotential Yield Improvement
Even at high overhead, significant yield improvementis possible
0.25
0.5
0.75
11.25
5%
10%15%
20%25%
0%
1%
2%
3%
4%
5%
6%
7%
Defect Density
Test Overhead
Yield Improvement
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PhotolithographyPhotolithography
� Decreasing feature size not accompanied by decreasing wavelength of light used
� 193nm and 157nm wavelengths most common in current technology
� Printing of features at 65nm, 45nm, and below uses off axis illumination, immersion lithography and more
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Lithography Basics (Lithography Basics (RayleighRayleigh Equation)Equation)
illum. λλλλ
Pmin = 1 ----λλλλ
NA
mask
lens
image
R = k1 ----
k1 min=0.5
λλλλ
NA
sinθθθθm = m ------λλλλ
P
P = m ----λλλλ
sinθθθθ
Rmin = 0.5 ----λλλλ
NA
1) resolution is controlled by λ and NA
2) improving resolution by increasing NA hurts DOF
3) k = 0.5 resolution limit is real physical barrier Courtesy L. Liebmann
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� DRC moving beyond simple distance metrics to shape-based approaches
SRAF SRAF –– Sub Resolution Assistance FeaturesSub Resolution Assistance Features
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Example shapeExample shape--based problem layoutbased problem layout
Feature will be a problem acrossa wide range ofgeometries
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DFM and DFYDFM and DFY
� Uniformity is good for manufacturing
� Example: arrays of bit cells
� Non-uniformity creates value
� Example: standard cells with different
layout
� DFM Challenge:Maintain manufacturability (enough uniformity) while allowing value (enough non-uniformity)
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Uniformity in PolysiliconUniformity in Polysilicon
� Compare SRAM array (left) with pre-DFM standard cell (right)� Uniform direction
� Uniform shapes
� Uniform spacing
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DFM is SubtleDFM is Subtle
� Major problems found early in a process generation
� Addressed by design rules
� Minor changes in layout cause significant changes in manufacturability and yield
� Examples: moving a wire a few nanometers, adding extra overlap around a contact, doubling a via
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Lithography SimulationLithography Simulation
� Simulate manufacturing process to identify problem areas (“hot spots”)
� Several commercial solutions available
� Sensitive process information can be encrypted
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Some DFM ChallengesSome DFM Challenges
� Forbidden pitches
� Caused by inability to place SRAF due to
conflict with neighboring shapes
� Shape-based problems, distance-based rule decks
� Complex rules built out of need to avoid
certain shapes combined with need to
express constraints in standard DRC terms
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DRC Spacing ExampleDRC Spacing Example
� DRC is yes/no� Silicon is continuous� DFM recommendations and waivers attempt to compensate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
Spacing
Rela
tive D
efe
ct P
robability
Waiver spacing
Minimum spacing
Recommended spacing
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Quantifying DFM: Critical AreaQuantifying DFM: Critical Area
� Critical area measures the area where the center of a fixed size circular defect can land and cause a short or an open
� Can be weighted by size distribution (typically 1/x3)
2.0um
0.4um
0.5um
0.1um
critical area = 2um x 0.1um
0.5um
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.1 0.15 0.2 0.25 0.3 0.35 0.4
Spacing
Rela
tive D
efe
ct
Pro
bab
ilit
y
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Weighted Critical AreaWeighted Critical Area
� Combine critical area and defect distribution into single value
� Integrate area under yellow curve for total value
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 100 200 300 400 500
separation (nm)
A.U
.
defect density
critical area
weighted critical area
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More open space
=Fewer shorts
Double contacts
=Fewer opens
Yield variation over timeYield variation over time
At any time, one or the other could be better
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Process improved continuallyProcess improved continually
� Fab engineers use a Pareto chart to find key problems
� Over time, systematic problems will be fixed, leaving random (defect-related) yield to dominate
0
2
4
6
8
10
12
Bridge
M2
Contact
Fail
Bridge
M1
Open
Poly
Bridge
Poly
Open V1 Bridge
M4
Bridge
M3
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VariabilityVariability
� Historically, test concerned with defects, validation concerned with design variation
� Process variability occupies a middle ground
� Small variation: expected
� Medium variation: needs to be tolerated by
design (margin)
� Large variation: needs to be found by test
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Sources of variabilitySources of variability
� Accounted for by standard device model � Lot-to-Lot
� Wafer-to-Wafer
� Die-to-Die
� More complex effects, sometimes included in model� Within Die examples
– Length variability
– Oxide variability
– Implant variability
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Example: Leakage and Gate LengthExample: Leakage and Gate Length
� Longer gate leads to higher Vt
� Small change in Vt (<100mV) leads to large change in Ioff (10X) with small change in performance (10%)
IOFFH
IOFFL
IDS
VTL VTH VG
Low VT
High VT
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Finding variability at wafer levelFinding variability at wafer level
� Reticle based variability� 2x2 reticle
� Patterning issue – likely sources masks, lithography
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� Chemical/physical aspects of process optimized for middle distance from center, can cause variability issues at center and edges� Examples include etch, polishing
Variability across a waferVariability across a wafer
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Radial variabilityRadial variability
� Starts in one region and works outwards� May be related to position in equipment
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Variability versus defectivityVariability versus defectivity
� Outliers and discontinuities may be defect related
likely defects
questionable
likely variability
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DFX DFX –– Design For ExcellenceDesign For Excellence
� DFX = multidimensional optimization
� DFX can be
� DFT: testability
� DFM: manufacturability
� DFY: yield
� DFR: reliability
� Optimization requires quantification
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Quantified Tradeoffs Quantified Tradeoffs –– DFM MetricsDFM Metrics
� Simple but effective
� Recommended rule classes
� No effect on area: implement all
� Affects area
– Find intermediate value point
� Example: contact-poly spacing
� Minimum DRC value: 80nm (0 credit)
� Recommended DRC value: 120nm (full credit)
� 80% of yield benefit achieved: 90nm (half credit)
� Sum and score across instances
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Example DFM Rules and MetricExample DFM Rules and Metric
0.05 on 4 0.05 on 4 0.05 on 4 0.05 on 4 sidessidessidessides
0.05 on 3 0.05 on 3 0.05 on 3 0.05 on 3 sidessidessidessides
0.05 on 2 0.05 on 2 0.05 on 2 0.05 on 2 sidessidessidessides
.1.1.1.14. Maximize contact 4. Maximize contact 4. Maximize contact 4. Maximize contact overlapoverlapoverlapoverlap
0.25 spacing0.25 spacing0.25 spacing0.25 spacing0.2 spacing0.2 spacing0.2 spacing0.2 spacing0.15 spacing0.15 spacing0.15 spacing0.15 spacing.2.2.2.23. Reduce critical area 3. Reduce critical area 3. Reduce critical area 3. Reduce critical area for poly gatesfor poly gatesfor poly gatesfor poly gates
Jog <0.05Jog <0.05Jog <0.05Jog <0.05Jog > 0.05Jog > 0.05Jog > 0.05Jog > 0.05Jog > 0.1Jog > 0.1Jog > 0.1Jog > 0.1.3.3.3.32. 2. 2. 2. Avoid jogs in polyAvoid jogs in polyAvoid jogs in polyAvoid jogs in poly
0.150.150.150.150.10.10.10.10.050.050.050.05.4.4.4.41. Increase line end 1. Increase line end 1. Increase line end 1. Increase line end
100%100%100%100%50%50%50%50%0%0%0%0%
ScoringScoringScoringScoringWeightWeightWeightWeightRuleRuleRuleRule
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3A
3B
1A1B
2B
2A 4A
4B
Metric in practiceMetric in practice
� Example rules:1. Line ends
2. Jogs
3. Spacing
4. Contact overlap
� Key:A=bestB=partial
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3A
3B
1A1B
2B
2A 4A
4B
1C
1C
4C
Improved LayoutImproved Layout� Metric improves for
1. Line ends
2. Jogs
3. Spacing
4. Contact overlap
� Some locations
cannot be
improved (“C”)
without increasing
area
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Concluding RemarksConcluding Remarks� Yield is a complex function of many
variables
� Repair can improve yield for some structures
� Photolithography and related challenges influence DFM rules
� Effective DFM more complicated than design rule checking allows
� Metrics can be developed to improve manufacturability
� Multidimensional optimization is the key to effective Design For Excellence (DFX)