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 proceedingConference contributionAcademicpeer-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 languageEnglish
Title of host publicationScientific Computing in Electrical Engineering SCEE 2010
EditorsB. Michielsen, J.R. Poirier
Place of PublicationBerlin
PublisherSpringer
Pages39-47
ISBN (Print)978-3-642-22452-2
DOIs
Publication statusPublished - 2012
Eventconference; SCEE 2010; 2010-09-19; 2010-09-24 -
Duration: 19 Sep 201024 Sep 2010

Publication series

NameMathematics in Industry
Volume16
ISSN (Print)1612-3956

Conference

Conferenceconference; SCEE 2010; 2010-09-19; 2010-09-24
Period19/09/1024/09/10
OtherSCEE 2010

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  • 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