Statistical timing analysis using levelized covariance propagation considering systematic and random variations of process parameters

Variability in process parameters is making accurate timing analysis of nano-scale integrated circuits an extremely challenging task. In this article, we propose a new algorithm for statistical static timing analysis (SSTA) using levelized covariance propagation (LCP). The algorithm simultaneously considers the effect of die-to-die variations in process parameters as well as within-die variation, including systematic and random variations. In order to efficiently handle complicated process variation models while contending with the arbitrary correlation among timing signals, we employ a compact form of the levelized statistical data structure. Furthermore, we propose two enhancements to the LCP algorithms to the make it practical for the analysis of large sized circuits. Results on several ISCAS'85 benchmark circuits in predictive 70nm technology show an average of 0.19% and 0.57% errors in the mean and standard deviation, respectively, of timing analysis using the proposed technique, as compared to the Monte Carlo-based approach.