An accurate PLL behavioral model for fast Monte Carlo analysis under process variation

Hierarchical statistical analysis using the regression-based approach is often used to improve the extremely expensive HSPICE Monte Carlo (MC) analysis. However, accurately fitting the regression equations requires many simulation samples. In this paper, an accurate behavioral Monte Carlo simulation (BMCS) approach to analyze PLL designs under process variation is developed by building a bottom-up behavioral modeling approach with an efficient extraction process. Using the accurate model, we also develop a modified sensitivity analysis for process variation effects to provide accurate enough results with less regression cost. As shown in the experimental results, we reduce the simulation time of HSPICE MC analysis from several weeks to several hours and still retain similar statistical results as in HSPICE MC simulation.