Importance sampling for determining SRAM yield and optimization with statistical constraint

E.J.W. Maten, ter; O. Wittich; A. Di Bucchianico; T.S. Doorn; T.G.J. Beelen

doi:10.1007/978-3-642-22453-9_5

Importance sampling for determining SRAM yield and optimization with statistical constraint

E.J.W. Maten, ter, O. Wittich, A. Di Bucchianico, T.S. Doorn, T.G.J. Beelen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.

Original language	English
Title of host publication	Scientific Computing in Electrical Engineering SCEE 2010
Editors	B. Michielsen, J.R. Poirier
Place of Publication	Berlin
Publisher	Springer
Pages	39-47
ISBN (Print)	978-3-642-22452-2
DOIs	https://doi.org/10.1007/978-3-642-22453-9_5
Publication status	Published - 2012
Event	conference; SCEE 2010; 2010-09-19; 2010-09-24 - Duration: 19 Sep 2010 → 24 Sep 2010

Publication series

Name	Mathematics in Industry
Volume	16
ISSN (Print)	1612-3956

Conference

Conference	conference; SCEE 2010; 2010-09-19; 2010-09-24
Period	19/09/10 → 24/09/10
Other	SCEE 2010

Access to Document

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Maten, ter, E. J. W., Wittich, O., Di Bucchianico, A., Doorn, T. S., & Beelen, T. G. J. (2012). Importance sampling for determining SRAM yield and optimization with statistical constraint. In B. Michielsen, & J. R. Poirier (Eds.), Scientific Computing in Electrical Engineering SCEE 2010 (pp. 39-47). (Mathematics in Industry; Vol. 16). Springer. https://doi.org/10.1007/978-3-642-22453-9_5

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abstract = "Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.",

author = "{Maten, ter}, E.J.W. and O. Wittich and {Di Bucchianico}, A. and T.S. Doorn and T.G.J. Beelen",

year = "2012",

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note = "conference; SCEE 2010; 2010-09-19; 2010-09-24 ; Conference date: 19-09-2010 Through 24-09-2010",

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Maten, ter, EJW, Wittich, O, Di Bucchianico, A, Doorn, TS & Beelen, TGJ 2012, Importance sampling for determining SRAM yield and optimization with statistical constraint. in B Michielsen & JR Poirier (eds), Scientific Computing in Electrical Engineering SCEE 2010. Mathematics in Industry, vol. 16, Springer, Berlin, pp. 39-47, conference; SCEE 2010; 2010-09-19; 2010-09-24, 19/09/10. https://doi.org/10.1007/978-3-642-22453-9_5

Importance sampling for determining SRAM yield and optimization with statistical constraint. / Maten, ter, E.J.W.; Wittich, O.; Di Bucchianico, A.; Doorn, T.S.; Beelen, T.G.J.

Scientific Computing in Electrical Engineering SCEE 2010. ed. / B. Michielsen; J.R. Poirier. Berlin : Springer, 2012. p. 39-47 (Mathematics in Industry; Vol. 16).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Importance sampling for determining SRAM yield and optimization with statistical constraint

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AU - Doorn, T.S.

AU - Beelen, T.G.J.

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AB - Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.

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BT - Scientific Computing in Electrical Engineering SCEE 2010

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A2 - Poirier, J.R.

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