Statistical Timing Analysis with AMECT: Asymptotic MAX/MIN Approximation and Extended Canonical Timing Model

State of the art statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated due to global source of variations and path reconvergence. To the best of our knowledge, no good solution is available dealing both types of correlations simultaneously. In this paper, we present a novel statistic timing algorithm, AMECT (Asymptotic MAX/MIN approximation & Extended Canonical Timing model), that produces accurate timing estimation by solving both correlation problems simultaneously. Specifically, AMECT uses a linear mixing operator to approximate the nonlinear MAX/MIN operator by moment matching and develops an extended canonical timing model to evaluate and decompose correlations between arbitrary timing variables. Finally, AMECT is implemented by an intelligent pruning method to enable tradeoff runtime with accuracy. Tested with ISCAS benchmark suites, AMECT shows both high accuracy and high performance compared with Monte Carlo simulation results: with distribution estimation error < 1.5% while with around 350X speed up on a circuit with 5355 gates.