A Yield Prediction Method Based on Nonparametric Statistical and Comparison With a Networks Method

Abstract

In this paper, we propose a circuit yield prediction method to improve the efficiency of circuit yield prediction. Both traditional Monte Carlo simulation and the current popular machine learning for yield prediction require a large number of circuit simulation results, which increases production costs. This method finds the data boundary points through high-dimensional kernel density estimation, and performs simulation calculations on these boundary values instead of the overall simulation. Experiments show that, compared with machine learning and Monte Carlo methods, the proposed algorithm can efficiently predict the yield of circuits.