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With process variation becoming a growing concern in deep submicron technologies, the ability to efficiently obtain an accurate estimate of failure probability of SRAM components is becoming a central issue. In this paper we present a general methodology for a fast and accurate evaluation of the failure probability of memory designs. The proposed statistical method, which we call importance sampling through norm minimization principle, reduces the variance of the estimator to produce quick estimates. It builds upon the importance sampling, while using a novel norm minimization principle inspired by the classical theory of Large Deviations. Our method can be applied for a wide class of problems, and our illustrative examples are the data retention voltage and the read/write failure tradeoff for 6T SRAM in 32 nm technology. The method yields computational savings on the order of 10000x over the standard Monte Carlo approach in the context of failure probability estimation for SRAM considered in this paper.

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