NDLI: A sparse image method for BEM capacitance extraction

Content Provider	IEEE Xplore Digital Library
Author	Krauter, B. Yu Xia Dengi, A. Pileggi, L.T.
Copyright Year	1996
Description	Author affiliation: IBM Corp., Austin, TX, USA (Krauter, B.)
Abstract	Boundary element methods (BEM) are often used for complex 3D capacitance extraction because of their efficiency, ease of data preparation, and automatic handling of open regions. BEM capacitance extraction, however, yields a dense set of linear equations that makes solving via direct matrix methods such as Gaussian elimination prohibitive for large problem sizes. Although iterative, multipole-accelerated techniques have produced dramatic improvements in BEM capacitance extraction, accurate sparse approximations of the electrostatic potential matrix are still desirable for the following reasons. First, the corresponding capacitance models are sufficient for a large number of analysis and design applications. Moreover, even when the utmost accuracy is required, sparse approximations can be used to precondition iterative solution methods. We propose a definition of electrostatic potential that can be used to formulate sparse approximations of the electrostatic potential matrix in both uniform and multilayered planar dielectrics. Any degree of sparsity can be obtained, and unlike conventional techniques which discard the smallest matrix terms, these approximations are provably positive definite for the troublesome cases with a uniform dielectric and without a groundplane.
Starting Page	357
Ending Page	362
File Size	677152
Page Count	6
File Format	PDF
ISBN	0780332946
ISSN	0738100X
DOI	10.1109/DAC.1996.545601
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Capacitance Sparse matrices Dielectrics Conductors Data mining Equations Shape Electrostatics Circuits Algorithm design and analysis
Content Type	Text
Resource Type	Article

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