A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials

Performance failure has become a significant threat to the reliability and robustness of analog circuits. In this article, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion unde...

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Main Authors: Tan, Sheldon X. D., Ren, Junyan, He, Lei, Gong, Fang, Liu, Xuexin, Yu, Hao
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2012
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Online Access:https://hdl.handle.net/10356/95526
http://hdl.handle.net/10220/8763
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-955262020-03-07T14:02:43Z A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials Tan, Sheldon X. D. Ren, Junyan He, Lei Gong, Fang Liu, Xuexin Yu, Hao School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Performance failure has become a significant threat to the reliability and robustness of analog circuits. In this article, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion under PVT variations and probabilistic distribution of transient response is solved. We further define performance yield and derive stochastic sensitivity for yield within the framework of SOP, and finally develop a gradient-based multiobjective optimization to improve yield while satisfying other performance constraints. Extensive experiments show that compared to Monte Carlo-based yield estimation, our NMC method achieves up to 700X speedup and maintains 98% accuracy. Furthermore, multiobjective optimization not only improves yield by up to 95.3% with performance constraints, it also provides better efficiency than other existing methods. Accepted version 2012-10-11T06:49:00Z 2019-12-06T19:16:33Z 2012-10-11T06:49:00Z 2019-12-06T19:16:33Z 2010 2010 Journal Article Gong, F., Liu, X., Yu, H., Tan, S. X. D., Ren, J., & He, L. (2012). A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials. ACM Transactions on Design Automation of Electronic Systems, 17(1). 1084-4309 https://hdl.handle.net/10356/95526 http://hdl.handle.net/10220/8763 10.1145/2071356.2071366 162550 en ACM transactions on design automation of electronic systems © 2012 ACM. This is the author created version of a work that has been peer reviewed and accepted for publication by ACM Transactions on Design Automation of Electronic Systems, ACM. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2071356.2071366]. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Tan, Sheldon X. D.
Ren, Junyan
He, Lei
Gong, Fang
Liu, Xuexin
Yu, Hao
A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
description Performance failure has become a significant threat to the reliability and robustness of analog circuits. In this article, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion under PVT variations and probabilistic distribution of transient response is solved. We further define performance yield and derive stochastic sensitivity for yield within the framework of SOP, and finally develop a gradient-based multiobjective optimization to improve yield while satisfying other performance constraints. Extensive experiments show that compared to Monte Carlo-based yield estimation, our NMC method achieves up to 700X speedup and maintains 98% accuracy. Furthermore, multiobjective optimization not only improves yield by up to 95.3% with performance constraints, it also provides better efficiency than other existing methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Tan, Sheldon X. D.
Ren, Junyan
He, Lei
Gong, Fang
Liu, Xuexin
Yu, Hao
format Article
author Tan, Sheldon X. D.
Ren, Junyan
He, Lei
Gong, Fang
Liu, Xuexin
Yu, Hao
author_sort Tan, Sheldon X. D.
title A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
title_short A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
title_full A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
title_fullStr A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
title_full_unstemmed A fast non-Monte-Carlo yield analysis and optimization by stochastic orthogonal polynomials
title_sort fast non-monte-carlo yield analysis and optimization by stochastic orthogonal polynomials
publishDate 2012
url https://hdl.handle.net/10356/95526
http://hdl.handle.net/10220/8763
_version_ 1681049668849500160