Model to Hardware Matching for nm Scale Technologies · 2009-12-29 · Model to Hardware Matching...
Transcript of Model to Hardware Matching for nm Scale Technologies · 2009-12-29 · Model to Hardware Matching...
Model to Hardware Matching for nm Scale Technologies
Sani R. Nassif Manager Tools & Technology Department
IBM Research – [email protected]
Model to Hardware Matching?
DFM Job DescriptionWhat is the real problem?Imagine 109 people being askedto form a human chain.
Maybe the population of Europe.With height and width of eachperson varying by ~50%.Maybe after some beer…
This is what we ask 1B highlyvariable MOSFETs to do!
And we want to predict howlong the chain is going to be,and how much food and beeris required etc…
And… we cannot prototype!We use predictive models, andneed them to match reality.
Realities and MotivationThe IC industry is built on a foundation of models for performance, power, yield, cost…
When models do not predict the correct outcome, we lose money.
Technology complexity near the end of scaling is exacerbating this “prediction gap”.
Investments in technology modeling and understanding are needed!
An Example from IBMOne part of a design was found to be ~15% slower than other parts.Models predict all parts of design are identical.Model/hardware mismatch!
Slower block limits FMAX.Faster block wastes power.
Part-2
Part-0
Part-1
Case study performed byAnne E. Gattiker
Power
Spee
d
Target 0
21
0
21
Model to Hardware SupportChip has 12 ring oscillators distributed across the die, and individually measurable.
Chip map with Ring Oscillator locations
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~200%
~30%
Strong correlations between:Part-0 and RO 1.Part-2 and RO 8.
Systematic offset between the ROs.Even though the two ROs are identical!
Conclusion:Within-die variability caused systematic spatial mismatch (mean shift + rotation).Magnitude of mismatch large and has –veimpact on design.
Systematic Offsetsx=
y line
RO 1 vs. RO 8
Why? (Historical Perspective)In the beginning, design was hard and only few were able to do it well. This was the age of “chip engineering”.
But with scaling-driven performance, it became possible to abstract design to a few simple rules. This was the age of “chip computer science”!
Intel 8080Intel 8080
Intel 4004Intel 4004Intel 4004
Technology ComplexityTechnology has become so complex that it is not well represented by “rules”.
Design / Technology interface information bandwidth needs are skyrocketing!
Expressing complex non-linear realities via rules is becoming difficult.
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500nm 250nm 130nm 65nm
Technology
# Design Rules
Technology Rules (History)Example: wire spacing…
S W
Wmin
WminWidth
Spa
cing
Smin
Legal Region
Two rules suffice to define the “correct”design space!
Technology Rule ExplosionImpact of CMP, Dishing, Erosion as well as lithography...
S W
Wmin
Width
Spa
cing
Four (or more) rules for the same situation in order to accommodate technology complexity!
Wmin Wmax
Smin
“Legal”Region
What Does This Change?Transistor performance is being determined by new physics and complex interactions.
Large variety in behaviors.Systematic & Random variability increasing.
Devices used for technology characterization becoming less typical…
Number of variants is exploding.Ability to bound all behaviors is compromised.
Result: model / hardware mismatch.Gap in our understanding of technology is relatively large and getting larger!
Where Does This Put Us?Our lack of understanding of technology is endangering the way we do design.
The “contract” between manufacturing and design can no longer be just BSIM+DRC.
These changes will have a dramatic impact on CAD and on the Foundry business.
More than just more OPC.
Much of what we observe is being driven by Much of what we observe is being driven by variabilityvariability……
Variability vs. UncertaintyVariability: known quantitative relationship to a source (readily modeled and simulated) – systematic!
Designer has option to null out impact.Example: power delivery (package + grid) noise.
Uncertainty: sources unknown, or model too difficult/costly to generate or simulate – random!
Usually treated by some type of worst-case analysis.Example: ΔVTH within die variation.
Lack of modeling infrastructure and/or resources often Lack of modeling infrastructure and/or resources often transforms variability to uncertainty.transforms variability to uncertainty.
Example: power grid noise when not assessed!Example: power grid noise when not assessed!
TaxonomyIn addition to systematic and random, we also differentiate variability according to source and distribution.
Physical(time scale 109 sec)
Environmental(time scale 10−9 sec)
Spatial ΔL, Δμ, ΔVT Noise coupling
Temporal NBTI, Hot-ē, electro-migration
VDD, T
Variability Interaction with DesignOld view: Die to die variation dominant
Imposed upon design (constant regardless of design).Predominantly random (e.g. wafer location effect).Well modeled via worst-case files.
New view: Within-die variation dominantCo-generated between design & process (depend on details of the design).
Predominantly systematic.Example: CMP-driven RC variation.
Response to VariabilityResponses to variability target different “spatial frequencies”.
No one response is sufficient to tackle the full impact, but all responses require accurate estimates of magnitude and impact.
30nm30nm
Shape-level
OPC/RET
3μm3μm
Librarylevel
regularity
300μm300μm
Fabric-based
regularity
300μm300μm
Chip-level
adaptation
VDD-2
VDD-0
VDD-1
Space
Variability CharacterizationA hard problem.
Requires a large investment in test and characterization in order to understand all aspects of physical, environmental, systematic, random, etc…Also, many factors are design dependent.
So often generic test structures are not useful predictors (e.g. analog circuits)!Number of variants exploding with “transistor weight” problem.
Silicon Information DensityThe efficiency with which we can perform precise variability characterization is going to become important.
No longer sufficient to do it once (technology
bring up). Need to continually model and re-evaluate.As EDA tools ramp up on understanding process, they will enable new methods of design optimization (e.g. during re-spins).
Need vastly more information from scarce Si & test resources (i.e. more density)!
Test Structure Quality?Three relative measures:
Number of individually measurable entities (FETs, ring oscillators, etc…).
Many entities ⇒ statistics!Test time (or test cost).
Lower cost ⇒ statistics!Generality of result: suitability for predicting design outcome.
Modeling & EDA.
Gen
eral
ity Desired Spot
Test
Time
Entities
Example: Device CharacterizationSmall number of devices with various dimensions.
Entities poorpoor.Typically measure many current / voltage points.
Slow test time poorpoor.Used to generate model parameters, which are the basis for everything else… (e.g. BSIM)
i.e. generality excellentexcellent.
Entities
Gen
eral
ity
Test Time
Example: Ring Oscillators (ROs)Few ROs per unit.
Entities poorpoor.Typically measure few frequency & IDD points.
Fast test time, goodgood.Useful to assess overall health of process, but result is unique to RO structure.
i.e. generality poor.
Entities
Gen
eral
ity
Test Time
Example: RO CollectionsVariety of ROs per unit.
Entities OKOK.Typically measure few frequency & IDD points.
Test goodgood.Useful to assess overall health of process, and since a variety of ROs are included, more can be learned.
i.e. generality OKOK.
Entities
Gen
eral
ity
Test Time
high and low Vt
nfet vs. pfet strength
nand nor
nfet Vt
inverter + passgate
Canonical view: Varied ring types versus basic inverter ring
Advanced Modeling Structure
SRAM sized devices arranged in an addressable manner.96 rows, 1000 columns – 96,000 total devices.Complete I/V possible.
DUT Array
Right LSSD bank
Row SenseCurrent Steering
Left LSSD bank
DUT Array
Bottom LSSD bank and column drivers
Top LSSD bank and column drivers
DUT Array
Right LSSD bank
Row SenseCurrent Steering
Left LSSD bank
DUT Array
Bottom LSSD bank and column drivers
Top LSSD bank and column drivers
96 rows
1000 columnsMeasI SinkI
Drain Voltage
Drain Clamp
Gate Voltage
Gate Clamp
96 rows
1000 columnsMeasI SinkI
Drain Voltage
Drain Clamp
Gate Voltage
Gate Clamp
Analog Interface
Packaged Chip(Clamp assembly for C4 contacts)
Digital (Scan) Interface
Analog Interface
Packaged Chip(Clamp assembly for C4 contacts)
Digital (Scan) Interface
Experimental Results
Measure full I/V range.Extract parameter statistics through least-squares based parameter extraction.
Leakage statistics.Lognormal distribution.No spatial correlation.Systematic effects do not appear due to regular layout.Random dopant effect is the dominant source of variation.
Siren Call: Variational ModelingDevice models caught between the need to provide nominal and statistical accuracy.
Nominal accuracy: ever more complex models with many fitting parameters and difficult-to-extract inter-dependencies.
Can you spell BSIM?Statistical accuracy: simple models that exhibit a physically significant and predictive correlation structure.
What is the right solution for this?What is the right solution for this?
Linking Variability & ResilienceMuch current work is focused on the immediate and short term impact of variability.
Examples: statistical timing analysis, CMP-aware routing, new approaches for function-based OPC etc…
But increasing variability will change the character of the impact it has on circuits.
What changes with increased variability?
Circuits can become permanently or intermittently defective.Failure dependence on operating environment makes test coverage very difficult to achieve.This can be viewed as the merger of failure modes due to structural (topological), and parametric (variability) defects.
Via ResistanceVia Resistance
1Ω 1MΩ
CircuitOK
CircuitNot OK
100Ω
Distribution of“Good” vias
Distribution of“Bad” vias
Current viadistribution
Fails that looklike opens!
Other factors, like the environment, make the failure region fuzzy and broad!
How Is This Happening?Increased process complexity and systematic variability.
Example: a VT of 0.25±0.1, a via of 20…200Ω.Multidimensional: combination of multiple sources of variations.
As variability increases, circuit performance passes from a degraded phase to a regime where failure becomes indistinguishable from hard (short/open) faults.
VT
RVIA
Fail
VT
RVIA Fail looks the same as an
“almost open”defect!
VDD & Temp can make this worse. Environment also
plays a part
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Metric is how many sigmas from nominal does failure occur!
Random/Systematic Via Variability
Via Resistance
Double vias appear to
behave fairly consistently
Single vias exhibit high
and systematicvariability
FailOK Adapt Degrade
Performance vs. Sigma
SigmaCircuit delay
exceedsspecification
Circuit delay beyond fixingby adaptation
Circuit doesnot invertany longer
time
outp
ut v
olta
ge
nominal
time
outp
ut v
olta
ge
nominal
time
outp
ut v
olta
ge
nominal
Performanceindistinguishable
from a “stuckat 1” fault!
A B C
Perf
orm
ance
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AB
CProcess σ point
Trend For a Simple Buffer
Simplest possible circuit (if this fails, everything else will).Performed analysis for 90nm, 65nm and 45nm.Clear trend in sigma!
Nominal VDDDelay@150%
VDD@+15%Delay@150%
No edgepropagation
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A
B
CProcess σ point
Technology Trend For a Simple Latch
Pervasive circuit crucial for correct logic operation.Performed analysis for 90nm, 65nm and 45nm.Clear trend in sigma!
Nominal VDDDelay@150%
VDD@+15%Delay@150%
No edgepropagation
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30 50 70 90 110
Technology Trend for an SRAMSRAM is known to be a more sensitive circuit… (lower σ).But, circuit heavily optimized for each technology.Much lower σ values + similar trend in sigma!
A
B
CProcess σ point
Nominal VCSDelay@150%
VCS@+15%Delay@150%
Cannot writeto cell
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30 50 70 90 110
Process σ point
Comparison of Circuits (@Point C)
Technology trend is modulated by circuit innovation and investment in analysis and optimization tools.Global trend remains clear!
SRAM is least robust,but much attentionsis devoted here, e.g.
redundancy & 8T
Latch is less robust+ suffers from SER
Buffer is most robust,unlikely to fail!
Variability and ResilienceWe will likely need wide-spread resilience at and beyond the 32nm technology node.How much resilience and/or adaptation will be determined by how well we understand the fabrication process.
Firm understanding of the sources and impact of systematic variation is needed.Magnitude of random or un-modeled variation will determine design margins (and design profit).Models are critical to both!
Role of ModelingModeling needs to become more aware of systematic and random variability.
As do other parts of the analysis flow, e.g. layout extraction.
We need the ability to mix empirical and physical capabilities in a clean manner…
As we learn to make better characterization structures, the opportunity for early semi-empirical modeling will expand!
Present LearningFuture Learning
ImprovedDesign…
ImprovedDesign…
Ultimate VisionEnable accurate model to hardware correlation and sophisticated design adaptation.
VirtualFab
VirtualFab
Parameter History
Performance History
Model toHardware Matching
Model toHardware Matching
Chip
Teststructure
Wafer0
102030405060
Lot 1 Lot 2 Lot 3 Lot 4 Lot 5 Lot 6
FmaxPowerYield
0.7
0.8
0.9
1
1.1
1.2
Lot 1 Lot 2 Lot 3 Lot 4 Lot 5 Lot 6
Target
VTHDLPSRO
SimulationParameters
& Tools
adaptDesignCycle
DesignCycle