A Fast and Robust Failure Analysis of Memory Circuits Using Adaptive Importance Sampling Method

Abstract

Performance failure has become a growing concern for the robustness and reliability of memory circuits. It is challenging to accurately estimate the extremely small failure probability when failed samples are distributed in multiple disjoint failure regions. In this paper, we develop an adaptive importance sampling (AlS) method. AIS has several iterations of sampling region adjusbnents, while existing methods pre-decide a static sampling distribution. By iteratively searching for failure regions, AIS may lead to better efficiency and accuracy. This is validated by our experiments. For SRAM cell with single failure region, AIS uses 5–10X fewer samples and reaches better accuracy when compared to several recent methods. For sense amplifier circuit with multiple failure regions, AIS is 4369X faster than MC without compromising accuracy, while other methods fail to cover all failure regions in our experiment