research-article

A fast estimation of SRAM failure rate using probability collectives

Authors:

Sina Basir-Kazeruni,

Lei HeAuthors Info & Claims

ISPD '12: Proceedings of the 2012 ACM international symposium on International Symposium on Physical Design

Pages 41 - 48

https://doi.org/10.1145/2160916.2160926

Published: 25 March 2012 Publication History

Abstract

Importance sampling is a popular approach to estimate rare event failures of SRAM cells. We propose to improve importance sampling by probability collectives. First, we use "Kullback-Leibler (KL) distance" to measure the distance between the optimal sampling distribution and the original sampling distribution of variable process parameters. Further, the probability collectives (PC) technique using immediate sampling is adapted to analytically minimize the KL distance and to obtain a sampling distribution as close to the optimal as possible. The proposed algorithm significantly accelerates the convergence of importance sampling. Experiments demonstrate that proposed algorithm is 5200X faster than the Monte Carlo approach and achieves more than $40X$ speedup over other existing state-of-the-art techniques without compromising estimation accuracy.

References

[1]

R. Heald and P. Wang, "Variability in sub-100nm SRAM designs," in Computer Aided Design, 2004. ICCAD-2004. IEEE/ACM International Conference on, pp. 347--352, 2004.

Digital Library

[2]

P. Girard, A. Bosio, L. Dilillo, P. S., and A. Virazel, "Advanced test methods for SRAMs: Effective solutions for dynamic fault detection in nanoscaled technologies," 2009.

Digital Library

[3]

K. Agarwal and S. Nassif, "Statistical analysis of SRAM cell stability," in Proceedings of the 43rd annual Design Automation Conference, DAC '06, pp. 57--62, 2006.

Digital Library

[4]

R. Kanj, R. Joshi, and S. Nassif, "Mixture importance sampling and its application to the analysis of SRAM designs in the presence of rare failure events," in Proceedings of the 43rd annual Design Automation Conference, DAC'06, pp. 69--72, 2006.

Digital Library

[5]

L. Dolecek, M. Qazi, D. Shah, and A. Chandrakasan, "Breaking the simulation barrier: SRAM evaluation through norm minimization," in Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design, ICCAD '08, pp. 322--329, 2008.

Digital Library

[6]

M. Qazi, M. Tikekar, L. Dolecek, D. Shah, and A. Chandrakasan, "Loop flattening and spherical sampling: Highly efficient model reduction techniques for SRAM yield analysis," in Design, Automation Test in Europe Conference Exhibition (DATE), 2010, pp. 801--806, 2010.

Digital Library

[7]

A. Singhee and R. Rutenbar, "Statistical Blockade: A novel method for very fast monte carlo simulation of rare circuit events, and its application," in Design, Automation Test in Europe Conference Exhibition, 2007. DATE '07, pp. 1--6, 2007.

Digital Library

[8]

K. Katayama, S. Hagiwara, H. Tsutsui, H. Ochi, and T. Sato, "Sequential importance sampling for low-probability and high-dimensional SRAM yield analysis," in IEEE/ACM International Conference on Computer-Aided Design, ICCAD '10, 2010.

Digital Library

[9]

C. Dong and X. Li, "Efficient SRAM failure rateprediction via Gibbs sampling," in Proceedings of the 43rd annual Design Automation Conference, DAC'11, 2011.

Digital Library

[10]

V. K. Rohatgi and A. K. M. Ehsanes Saleh, "An introduction to probability and statistics," Wiley-Interscience, 2000.

[11]

T. M. Cover and J. A. Thomas, "Elements of information theory," John Wiley and Sons, 1991.

Digital Library

[12]

D. Rajnarayan, D. H. Wolpert, and I. Kroo, "Optimization under uncertainty using probability collectives," 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006.

[13]

D. Rajnarayan, I. Kroo, and D. H. Wolpert, "Probability collectives for optimization of computer simulations," AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2007.

[14]

A. Ridder and R. Y. Rubinstein, "Minimum cross-entropy methods for rare-event simulation," Simulation: Transactions of the Society for Modeling and Simulation International, vol. 83, pp. 769--784, 2007.

Digital Library

[15]

T. H. de Mello, "A study on the cross-entropy method for rare event probability estimation," INFORMS Journal on Computing, vol. 19, no. 3, pp. 381--394, 2007.

Digital Library

[16]

P. T. de Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, "A tutorial on the cross entropy method," Annals of Operations Research, vol. 134, pp. 19--67, 2005.

[17]

H. Niederreiter, "Random number generation and quasi-monte carlo methods," Society for Industrial and Applied Mathematics, 1992.

Digital Library

[18]

D. Mukherjee, H. K. Mondal, and B. Reddy, "Static noise margin analysis of SRAM cell for high speed application," International Journal of Computer Science Issues, 2010.

Cited By

Zuo XGupta S(2020)Aging-resilient SRAM design: an end-to-end framework2020 IEEE 38th VLSI Test Symposium (VTS)10.1109/VTS48691.2020.9107571(1-6)Online publication date: Apr-2020
https://doi.org/10.1109/VTS48691.2020.9107571
Haine TSegers JFlandre DBol D(2018)Gradient importance sampling: An efficient statistical extraction methodology of high-sigma SRAM dynamic characteristics2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2018.8342002(195-200)Online publication date: Mar-2018
https://doi.org/10.23919/DATE.2018.8342002
Zhai JYan CWang SZhou DZhou HZeng X(2018)An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield AnalysisACM Transactions on Design Automation of Electronic Systems10.1145/317486623:3(1-23)Online publication date: 16-Mar-2018
https://dl.acm.org/doi/10.1145/3174866
Show More Cited By

Index Terms

A fast estimation of SRAM failure rate using probability collectives
1. Hardware
  1. Hardware validation
    1. Functional verification

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Published In

cover image ACM Conferences

ISPD '12: Proceedings of the 2012 ACM international symposium on International Symposium on Physical Design

March 2012

220 pages

ISBN:9781450311670

DOI:10.1145/2160916

General Chair:
Jiang Hu
Texas A&M University
,
Program Chair:
Cheng-Kok Koh
Purdue University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2012

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ISPD'12: International Symposium on Physical Design

March 25 - 28, 2012

California, Napa, USA

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Overall Acceptance Rate 62 of 172 submissions, 36%

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Cited By

Zuo XGupta S(2020)Aging-resilient SRAM design: an end-to-end framework2020 IEEE 38th VLSI Test Symposium (VTS)10.1109/VTS48691.2020.9107571(1-6)Online publication date: Apr-2020
https://doi.org/10.1109/VTS48691.2020.9107571
Haine TSegers JFlandre DBol D(2018)Gradient importance sampling: An efficient statistical extraction methodology of high-sigma SRAM dynamic characteristics2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2018.8342002(195-200)Online publication date: Mar-2018
https://doi.org/10.23919/DATE.2018.8342002
Zhai JYan CWang SZhou DZhou HZeng X(2018)An Efficient Non-Gaussian Sampling Method for High Sigma SRAM Yield AnalysisACM Transactions on Design Automation of Electronic Systems10.1145/317486623:3(1-23)Online publication date: 16-Mar-2018
https://dl.acm.org/doi/10.1145/3174866
Rabiei EWhite MMosleh ALyer SWoo J(2018)Component Reliability Modeling Through the Use of Bayesian Networks and Applied Physics-based Models2018 Annual Reliability and Maintainability Symposium (RAMS)10.1109/RAM.2018.8462986(1-7)Online publication date: Jan-2018
https://doi.org/10.1109/RAM.2018.8462986
Xuan Zuo Gupta S(2017)Asymmetric sizing: An effective design approach for SRAM cells against BTI aging2017 IEEE 35th VLSI Test Symposium (VTS)10.1109/VTS.2017.7928959(1-6)Online publication date: Apr-2017
https://doi.org/10.1109/VTS.2017.7928959
Jizhe Zhang Gupta S(2017)Wordline overdriving test: An effective predictive testing method for SRAMs against BTI aging2017 18th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED.2017.7918358(454-459)Online publication date: Mar-2017
https://doi.org/10.1109/ISQED.2017.7918358
Zhang JGupta S(2016)SRAM yield-per-area optimization under spatially-correlated process variation2016 IEEE 34th VLSI Test Symposium (VTS)10.1109/VTS.2016.7477304(1-6)Online publication date: Apr-2016
https://doi.org/10.1109/VTS.2016.7477304
Zhang JGupta S(2016)Using hardware testing approaches to improve software testing: Undetectable mutant identification2016 IEEE 34th VLSI Test Symposium (VTS)10.1109/VTS.2016.7477281(1-6)Online publication date: Apr-2016
https://doi.org/10.1109/VTS.2016.7477281
Beiu VTache M(2015)Revisiting delay variations statistically through an example2015 International Semiconductor Conference (CAS)10.1109/SMICND.2015.7355200(179-182)Online publication date: Oct-2015
https://doi.org/10.1109/SMICND.2015.7355200
Beiu VTache M(2015)Statistical analysis of static noise margins2015 European Conference on Circuit Theory and Design (ECCTD)10.1109/ECCTD.2015.7300090(1-4)Online publication date: Aug-2015
https://doi.org/10.1109/ECCTD.2015.7300090
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