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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Bucklew, J.A.: Introduction to Rare Event Simulation. Springer, Berlin (2004)
Doorn, T.S., ter Maten, E.J.W., Croon, J.A., Di Bucchianico, A., Wittich, O.: Importance Sampling Monte Carlo simulation for accurate estimation of SRAM yield. In: Proceedings of the IEEE ESSCIRC’08, 34th European Solid-State Circuits Conference, Edinburgh, Scotland, pp. 230–233 (2008)
Doorn, T.S., Croon, J.A., ter Maten, E.J.W., Di Bucchianico, A.: A yield statistical centric design method for optimization of the SRAM active column. In: Proceedings of the IEEE ESSCIRC’09, 35th European Solid-State Circuits Conference, Athens, Greece, pp. 352–355 (2009)
de Haan, L., Ferreira, A.: Extreme Value Theory. Springer, Berlin (2006)
den Hollander, F.: Large Deviations. Fields Institute Monographs 14, The Fields Institute for Research in Math. Sc. and AMS, Providence, RI (2000)
ter Maten, E.J.W., Doorn, T.S., Croon, J.A., Bargagli, A., Di Bucchianico, A., Wittich, O.: Importance Sampling for high speed statistical Monte-Carlo simulations – Designing very high yield SRAM for nanometer technologies with high variability. TUE-CASA 2009-37, TU Eindhoven (2009), http://www.win.tue.nl/analysis/reports/rana09-37.pdf
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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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