STAC: statistical timing analysis with correlation

Abstract

Current technology trends have led to the growing impact of both inter-die and intra-die process variations on circuit performance. While it is imperative to model parameter variations for sub-100nm technologies to produce an upper bound prediction on timing, it is equally important to consider the correlation of these variations for the bound to be useful. In this paper we present an efficient block-based statistical static timing analysis algorithm that can account for correlations from process parameters and re-converging paths. The algorithm can also accommodate dominant interconnect coupling effects to provide an accurate compilation of statistical timing information. The generality and efficiency for the proposed algorithm is obtained from a novel simplification technique that is derived from the statistical independence theories and principal component analysis (PCA) methods. The technique significantly reduces the cost for mean, variance and covariance computation of a set of correlated random variables.