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A parallel and incremental extraction of variational capacitance with stochastic geometric moments

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A parallel and incremental extraction of variational capacitance with stochastic geometric moments

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Title: A parallel and incremental extraction of variational capacitance with stochastic geometric moments
Author: Gong, Fang; Yu, Hao; Wang, Lingli; He, Lei
Copyright year: 2011
Abstract: This paper presents a parallel and incremental solver for stochastic capacitance extraction. The random geometrical variation is described by stochastic geometrical moments, which lead to a densely augmented system equation. To efficiently extract the capacitance and solve the system equation, a parallel fast-multipole-method (FMM) is developed in the framework of stochastic geometrical moments. This can efficiently estimate the stochastic potential interaction and its matrix-vector product (MVP) with charge. Moreover, a generalized minimal residual (GMRES) method with incremental update is developed to calculate both the nominal value and the variance. Our overall extraction show is called piCAP. A number of experiments show that piCAP efficiently handles a large-scale on-chip capacitance extraction with variations. Specifically, a parallel MVP in piCAP is up 3 × to faster than a serial MVP, and an incremental GMRES in piCAP is up to 15× faster than non-incremental GMRES methods.
Subject: DRNTU::Engineering::Electrical and electronic engineering.
Type: Journal Article
Series/ Journal Title: IEEE transactions on very large scale integration (VLSI) systems
School: School of Electrical and Electronic Engineering
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TVLSI.2011.2161352].
Version: Accepted version

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