Process-Variation Statistical Modeling for VLSI Timing Analysis

Jui-Hsiang Liu1,  Lumdo Chen2,  Charlie Chung-Ping Chen1
1EE Department, National Taiwan University, Taiwan, 2UMC, Taiwan


The most recent research of SSTA requires accurate non-Gaussian data processing modeling. Although quadratic Gaussian forms have been proposed to data modeling, limitations are imposed to ensure real coefficients. However, there are even more difficult distributions which can not be modeled by previous one. In this paper, we are going to solve these problems by allowing complex coefficients and higher order Gaussian polynomials with a PDF recovering scheme. Experimental results show how our methods and new algorithms expose some enhancements in both accuracy and versatility.