Variation-aware interconnect extraction using statistical moment preserving model order reduction

—1 In this paper we present a stochastic model order reduction technique for interconnect extraction in the presence of process variabilities, i.e. variation-aware extraction. It is becoming increasingly evident that sampling based methods for variation-aware extraction are more efficient than more computationally complex techniques such as stochastic Galerkin method or the Neumann expansion. However, one of the remaining computational challenges of sampling based methods is how to simultaneously and efficiently solve the large number of linear systems corresponding to each different sample point. In this paper, we present a stochastic model reduction technique that exploits the similarity among the different solves to reduce the computational complexity of subsequent solves. We first suggest how to build a projection matrix such that the statistical moments and/or the coefficients of the projection of the stochastic vector on some orthogonal polynomials are preserved. We further...

• Type: Conference
• Year: 2010
• Where: DATE
• Authors: Tarek A. El-Moselhy, Luca Daniel
• Keywords: Capacitance Extraction, DATE 2010, Hardware, Stochastic, Variation-aware Extraction