Abstract
The impact of parameter variations on timing due to process variations has become significant in recent years. In this paper, we present a statistical timing analysis (STA) framework with quadratic gate delay models that also captures spatial correlations. Our technique does not make any assumption about the distribution of the parameter variations, gate delays, and arrival times. We propose a Taylor-series expansion-based quadratic representation of gate delays and arrival times which are able to effectively capture the nonlinear dependencies that arise due to increasing parameter variations. In order to reduce the computational complexity introduced due to quadratic modeling during STA, we also propose an efficient linear modeling driven quadratic STA scheme. We ran two sets of experiments assuming the global parameters to have uniform and Gaussian distributions, respectively. On an average, the quadratic STA scheme had 20.5 x speedup in runtime as compared to Monte Carlo simulations with an rms error of 0.00135 units between the two timing cummulative density functions (CDFs). The linear modeling driven quadratic STA scheme had 51.5 x speedup in runtime as compared to Monte Carlo simulations with an rms error of 0.0015 units between the two CDFs. Our proposed technique is generic and can be applied to arbitrary variations in the underlying parameters under any spatial correlation model.