Importance Splitting Sample Point Reuse for Efficient Memory Yield Estimation

In this paper, we propose and evaluate efficient sample point reuse methodologies for Importance Splitting- based yield estimation of memory designs to assess the impact of manufacturing variability induced design center shifts. The proposed methodologies enable yield estimation at almost no additional simulation cost to that incurred due to studying the yield at the nominal design center. In order to unbias the Importance Splitting conditional probabilities with respect to the manufacturing variability centers, we evaluate three techniques that rely on Importance Sampling, center projections and geometric ratios. The geometric-based approach is shown to be most accurate for center shifts up-to three sigma away from the nominal. The proposed reuse methodologies achieve up-to 4 orders of magnitude speedup for the analysis of a 132-D 16nm SRAM design compared to traditional Importance Splitting.