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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
TitelScientific Computing in Electrical Engineering SCEE 2010
RedacteurenB. Michielsen, J.R. Poirier
Plaats van productieBerlin
UitgeverijSpringer
Pagina's39-47
ISBN van geprinte versie978-3-642-22452-2
DOI's
StatusGepubliceerd - 2012
Evenementconference; SCEE 2010; 2010-09-19; 2010-09-24 -
Duur: 19 sep 201024 sep 2010

Publicatie series

NaamMathematics in Industry
Volume16
ISSN van geprinte versie1612-3956

Congres

Congresconference; SCEE 2010; 2010-09-19; 2010-09-24
Periode19/09/1024/09/10
AnderSCEE 2010

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  • Citeer dit

    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 (editors), Scientific Computing in Electrical Engineering SCEE 2010 (blz. 39-47). (Mathematics in Industry; Vol. 16). Springer. https://doi.org/10.1007/978-3-642-22453-9_5