Efficient time-domain variability analysis using parameterized model-order reduction

Abstract

A fast algorithm is presented for time-domain statistical analysis of large circuits with multiple stochastic parameters. The proposed technique is based on Numerical Inversion of Laplace Transform and does not require explicit presentation of the dynamic model in the form of set of differential equations. As a result, the moment vectors associated with frequency can be excluded when forming the moments subspace, leading to much smaller reduced models. In addition, the algorithm is highly parallelizable compared to time-stepping numerical integration techniques.

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