Correlation-aware statistical timing analysis with non-gaussian delay distributions
Process variations have a growing impact on circuit performance for today's integrated circuit (IC) technologies. The Non-Gaussian delay distributions as well as the correlations among delays make statistical timing analysis more challenging than ever. In this paper, we present an efficient block-based statistical timing analysis approach with linear complexity with respect to the circuit size, which can accurately predict Non-Gaussian delay distributions from realistic nonlinear gate and interconnect delay models. This approach accounts for all correlations, from manufacturing process dependence, to re-convergent circuit paths to produce more accurate statistical timing predictions. With this approach, circuit designers can have increased confidence in the variation estimates, at a low additional computation cost. Copyright 2005 ACM.
