Statistical timing analysis with correlated non-gaussian parameters using independent component analysis

We propose a scalable and efficient parameterized block-based statistical static timing analysis algorithm incorporating both Gaussian and non-Gaussian parameter distributions, capturing spatial correlations using a grid-based model. As a preprocessing step, we employ independent component analysis to transform the set of correlated non-Gaussian parameters to a basis set of parameters that are statistically independent, and principal components analysis to orthogonalize the Gaussian parameters. The procedure requires minimal input information: given the moments of the variational parameters, we use a Pad?e approximation-based moment matching scheme to generate the distributions of the random variables representing the signal arrival times, and preserve correlation information by propagating arrival times in a canonical form. For the ISCAS89 benchmark circuits, as compared to Monte Carlo simulations, we obtain average errors of 0.99% and 2.05%, respectively, in the mean and standard de...

• Type: Conference
• Year: 2006
• Where: DAC
• Authors: Jaskirat Singh, Sachin S. Sapatnekar
• Keywords: Correlated Non-Gaussian Parameters, DAC 2006, Design Automation, Gaussian Parameters, Variational Parameters