Accurate and Analytical Statistical Spatial Correlation Modeling Based on Singular Value Decomposition for VLSI DFM Applications

Abstract

With the significant advancement of statistical timing and yield analysis algorithms, there is a strong need for accurate and analytical spatial correlation models. In this paper, we propose a novel spatial correlation modeling method that can not only capture the general spatial correlation relationship but also can generate highly accurate and analytical models. Our method, based on singular value decomposition, can generate sequences of polynomial weighted by the singular values. Experimental results from foundry measurement data show that our proposed approach is 3x accuracy improvement over several distance based spatial correlation modeling methods.