Statistical timing analysis with path reconvergence and spatial correlations

State of the art statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated. Spatial correlation and correlation caused by path reconvergence are among those which are most difficult to deal with. Existing methods treating these correlations will either suffer from high computational complexity or significant errors. In this paper, we present a sensitivity pruning method which significantly reduces the computational cost to consider path reconvergence correlation. We also develop an accurate and efficient model to deal with the spatial correlation