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.
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ter Maten, 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: Michielsen, B., Poirier, JR. (eds) Scientific Computing in Electrical Engineering SCEE 2010. Mathematics in Industry(), vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22453-9_5
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DOI: https://doi.org/10.1007/978-3-642-22453-9_5
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