Enhancement of grid-based spatially-correlated variability modeling for improving SSTA accuracy

Statistical timing analysis for manufacturing variability requires modeling of spatially-correlated variation. Common grid-based modeling for spatially-correlated variability involves a trade-off between accuracy and computational cost, especially for PCA (principal component analysis). This paper proposes to spatially interpolate variation coefficients for improving accuracy instead of fining spatial grids. Experimental results show that the spatial interpolation realizes a continuous expression of spatial correlation, and reduces the maximum error of timing estimates that originates from sparse spatial grids For attaining the same accuracy, the proposed interpolation reduced CPU time for PCA by 97.7% in a test case.