A Non-Gaussian Adaptive Importance Sampling Method for High-Dimensional and Multi-Failure-Region Yield Analysis

Rare-event yield analysis is challenging for high-dimensional circuit cases. In this paper, we propose a non-Gaussian adaptive importance sampling (NGAIS) method. In order to approximate the failure region in high-dimensional space, we model it as a mixture of von Mises-Fisher distributions. We formulate the parameter estimation problem as a maximum likelihood estimation problem, and then solve with expectation-maximization algorithm. Experiments on bit cell, amplifier and SRAM column circuit validate that the proposed NGAIS method outperforms other state-of-the-art approaches in terms of accuracy and efficiency.