Process-Variation Statistical Modeling for VLSI Timing Analysis

Abstract

SSTA requires accurate statistical distribution models of non-Gaussian random variables of process parameters and timing variables. Traditional quadratic Gaussian model has been shown to have some serious limitations. In particular, it limits the range of skewness that can be modeled and it can not model the kurtosis. In this paper, we presented complex-coefficient quadratic Gaussian polynomial model and higher order Gaussian polynomial model to resolve these difficulties. Experimental results show how our methods and new algorithms expose some enhancements in both accuracy and versatility.