Hybrid Importance Splitting Importance Sampling Methodology for Fast Yield Analysis of Memory Designs

Rare fail event estimation methodologies suffer from inefficiency when dealing with high dimensional design space problems. Importance splitting overcomes this complexity by recursively computing the rare fail probability as a product of larger conditional probabilities in the 1-D performance metric space. Its efficiency, however, drops as the events become rarer. In this work, we propose a novel hybrid Importance Sampling Importance Splitting methodology for purposes of rare fail event estimation of high-dimensional memory designs. In this context, we propose and evaluate two methods for unbiasing the estimate, a geometric ratio-based and an Importance Sampling-based methodology. We demonstrate 3-5X reduction in runtime for both theoretical and 16nm SRAM design applications compared to traditional Importance Splitting approaches.