Statistical framework for technology m odel product co design and convergence
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Statistical Framework for Technology- M odel-Product Co-Design and Convergence. Design Automation Conference June 6 , 200 7. Choongyeun Cho † , Daeik Kim † , Jonghae Kim † , Jean-Olivier Plouchart ‡ , Robert Trzcinski ‡

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Statistical Framework for Technology- M odel-Product Co-Design and Convergence

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Statistical framework for technology m odel product co design and convergence

Statistical Framework for Technology-Model-ProductCo-Design and Convergence

Design Automation Conference

June6,2007

  • Choongyeun Cho†, Daeik Kim†, Jonghae Kim†, Jean-Olivier Plouchart‡, Robert Trzcinski‡

  • †IBM Semiconductor R&D Center‡IBM T.J. Watson Research Center

29-2ebff_ver2007_06_06_05:18


Overview

Overview

  • Motivation

    • Current / proposed approaches

  • Proposed methodology

    • Statistical framework for tech-model-product co-design

  • Application

    • RF product development in three evolving 65nm SOI technologies

  • Conclusions

    • Summary and contributions


Overview1

Overview

  • Motivation

    • Current / proposed methodologies

  • Proposed methodology

    • Statistical framework for tech-model-product co-design

  • Application

    • RF product development in three evolving 65nm SOI technologies

  • Conclusions

    • Summary and contributions


Current ic development

Migration

P0

P0

Product

Model

Inter-pretation

T0

T1

Model

(Inter-pretation)

Model

Bench-mark

B0

B0

Migration

Mask delivery

Measure

Measure

Feedback

Feedback

Gen 1

Node 1

Process

Preparation

Mask

FEOL

BEOL

Prep

Mask

Gen 2

Node 0

Time

Current IC Development

  • Process, model and product are developed in parallel

Difficult to link from product to model/process


Current ic development1

Model

Process

Current IC Development

  • Ring oscillator (RO) is common vehicle for model calibration and product benchmarking, yet may not capture complex operation of product circuit

Benchmark

Interpretation

Feedback

Co-Design

Measurement

Model

Measurement

Model

Product

Feedback

Interpretation


Proposed approach

Proposed Approach

  • Statistical framework

    • Process variability is statistical by nature

    • Measurements from manufacturing-in-line electrical tests are exploited

    • Hardware data is capable of capturing complicated behavior of product

  • Systematic perspective for co-design

    • Translation from one component to another leading to rapid convergence of tech-model-product

  • Benefits

    • Accelerate yield learning

    • Reduce time-to-market and development cost


Overview2

Overview

  • Motivation

  • Proposed methodology

    • Statistical framework for tech-model-product co-design

      • Cross-correlation analysis

      • Statistical yield estimation

      • Variability decomposition

  • Application

  • Conclusions


Cross correlation analysis

Cross-Correlation Analysis

  • Correlate circuit performance with device parameters

    • Large volume of in-line electrical test data is often available

    • Links device characteristics to product performance/yield

    • For highly correlated parameters, analyze sensitivity

  • For example: Fosc vs Vth samples from one lot

xcorr=85%

Product FOM

(Fosc)

Sensitivity=3GHz/V

Device characteristic (Vth)


Cross correlation analysis1

Cross-Correlation Analysis

  • Statistical significance is determined:

    • E.g. 90% sample correlation using 60 chips guarantees84~94% correlation range within 95% confidence interval

  • Once most correlated device characteristics are identified, the information is fed to:

    • Design side: make more tolerable to the device parameter (Design-for-yield)

    • Technology side: control and monitor a particular process

    • Model side: model-to-hardware correlation (MHC) and calibration


Statistical yield estimation

Statistical Yield Estimation

  • “Performance (parametric) yield” defined as probability that predefined design specs are met:

  • Using simulation or hardware data, estimateperformance yield

    • Fit design parameter samples with simple (Gaussian) distribution

    • Yield is predicted as:Yield(x) = 1 – CDF(x) where x=minimum design requirement


Statistical yield estimation1

Statistical Yield Estimation

  • Example: a static inverter-based RO in 90nm tech

    • Target max delay=15ps, target max active current=1mA

Yield=90%

Delay

Delay


Variability decomposition

Variability Decomposition

  • Decompose process variation in die-to-die andwafer-to-wafer components:

    • Using only a collection of manufacturing electrical test data

    • No physical model for process variation

  • Benefits:

    • Allow quick visualization and analysis of systematic variability

    • Monitor lot characteristics, and impact of process recipe change

    • Efficient sampling for measurement

      (Details available in Cho, et al., ISQED’07)


Variability decomposition1

Variability Decomposition

Collection of in-line test data (FET’s, SRAM, cap, etc)

Standardize

  • Utilize principal component analysis (PCA)

  • Can generalize for other variation ranges (within-die andlot-to-lot)

  • Fast run-time

Screen data

Find first PC

for D2D variation

Find first PC

for W2W variation

Take PC

with larger variance

Process variation

Subtract this PC

space from

original data


Variability decomposition example

Variability Decomposition Example

  • Input: 1000+ parameters (FET, RO, cap, res, …) in65nm SOI tech

1stW2W PC

30

2nd W2W PC

3rd W2W PC

20

10

Systematic variation

0

-10

-20

0

5

10

15

Most dominating die-to-die variation

Wafer index

First three wafer-to-wafer variations


Overview3

Overview

  • Motivation

    • Current / proposed methodologies

  • Proposed methodology

    • Statistical framework for tech-model-product co-design

  • Application

    • RF product development in three evolving 65nm SOI technologies

  • Conclusions

    • Summary and contributions


Application background

Application: Background

  • Current-mode logic (CML) current-controlled oscillator (ICO) was developed

    • Critical function block in microprocessor clocking

    • Migrated from 90nm to 65nm

    • Challenging design issues: reduced Vdd headroom, increased nonlinearity/variation/leakage.


Product design decision

corr=98%

Product Design Decision

  • Device tuning via statistical measurements

    • Floating body (FB) vs body contacted (BC) in differential pair NFETs

FB

Gen1 FB

Correlation=98%

Gen1 BC

27% boost

FB

BC

BC


Cross correlation analysis2

Cross-Correlation Analysis

  • Identified critical process variation

    • Threshold voltage correlated with Fosc reduced

Gen3

xcorr=13%

Gen2

xcorr=94%


Variability decomposition2

Variability Decomposition

  • Dominant die-to-die variations analyzed forthree tech generations

    • Visualize how technology stabilizes.

Generation 1 (Pre-production)

Generation2

Generation3


Statistical yield estimation2

Statistical Yield Estimation

  • Predicted yield for three generations

    • Calculated with Gaussian PDF fits

100

Gen 1 BC

99

%

Gen 1 FB

80

64

%

Gen 2

47

%

60

Gen 3

Performance yield (%)

40

20

0.3%

Target=12GHz

0

8

10

12

14

16

18

Fosc

(GHz)


Application summary

Application Summary

  • ICO of 64-bit processor developed using proposed method across three 65nm tech generations, and met design specs in nominal and variation

15

Target Fosc=12GHz

(%)

Fosc

10

Mean Fosc (GHz)

10

8

Fosc

Target Std=8%

s

6

Both Fosc,Std failed

Fosc passed, Std failed

Fosc,Std passed

Target

Gen1

Gen2

Gen3


Overview4

Overview

  • Motivation

    • Current / proposed methodologies

  • Proposed methodology

    • Statistical framework for tech-model-product co-design

  • Application

    • RF product development in three evolving 65nm SOI technologies

  • Conclusions

    • Summary and contributions


Conclusions

Conclusions

  • A simple statistical framework is presented to expedite co-design of tech-model-product, based on three tools:

    • Cross-correlation analysis: identifies device characteristicsmost related/sensitive to product performance

    • Statistical yield estimation: predicts yield based on HW data

    • Variability decomposition: separates systematic variabilityfor analysis and visualization

  • Case study: ICO of microprocessor

    • Yield enhancement from 47% to 99% over three 65nm tech generations


Thank you

Thank you.


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