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	Scientific Computing in Electrical Engineering, SCEE 2010 - Toulouse, France Duration: 19 Sep 2010 → 24 Sep 2010 https://scee-conferences.org/

Publication series

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

Conference

Conference	Scientific Computing in Electrical Engineering, SCEE 2010
Country/Territory	France
City	Toulouse
Period	19/09/10 → 24/09/10
Other	SCEE 2010
Internet address	https://scee-conferences.org/

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

Cite this

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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booktitle = "Scientific Computing in Electrical Engineering SCEE 2010",

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note = "Scientific Computing in Electrical Engineering, SCEE 2010 ; 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, Scientific Computing in Electrical Engineering, SCEE 2010, Toulouse, France, 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

TY - GEN

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

AU - Maten, ter, E.J.W.

AU - Wittich, O.

AU - Di Bucchianico, A.

AU - Doorn, T.S.

AU - Beelen, T.G.J.

PY - 2012

Y1 - 2012

N2 - 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.

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.

U2 - 10.1007/978-3-642-22453-9_5

DO - 10.1007/978-3-642-22453-9_5

M3 - Conference contribution

SN - 978-3-642-22452-2

T3 - Mathematics in Industry

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EP - 47

BT - Scientific Computing in Electrical Engineering SCEE 2010

A2 - Michielsen, B.

A2 - Poirier, J.R.

PB - Springer

CY - Berlin

T2 - Scientific Computing in Electrical Engineering, SCEE 2010

Y2 - 19 September 2010 through 24 September 2010

ER -

Importance sampling for determining SRAM yield and optimization with statistical constraint

Abstract

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