BNN-YEO: an efficient Bayesian Neural Network for yield estimation and optimization

Yield estimation and optimization is ubiquitous in modern circuit design but remains elusive for large-scale chips. This is largely due to the mounting cost of transistor-level simulation and one's often limited resources. In this study, we propose a novel framework to estimate and optimize yield using Bayesian Neural Network (BNN-YEO). By coupling machine learning method with Bayesian network, our approach can effectively integrate prior knowledge and is unaffected by the overfitting problem prevalent in most surrogate models. With the introduction of a smooth approximation of the indicator function, it incorporates gradient information to facilitate global yield optimization. We examine its effectiveness via numerical experiments on 6T SRAM and found that BNN-YEO provides 100x speedup (in terms of SPICE simulations) over standard Monte Carlo in yield estimation, and 20x faster than the state-of-the-art method for total yield estimation and optimization with improved accuracy.