IEICE TRANSACTIONS on Fundamentals
Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method
Summary :
The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.9 pp.2419-2425
- Publication Date
- 2008/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e91-a.9.2419
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
- Analysis, Modelng and Simulation
Authors
Keyword
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Yuichi TANJI, "Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 9, pp. 2419-2425, September 2008, doi: 10.1093/ietfec/e91-a.9.2419.
Abstract: The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.9.2419/_p
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@ARTICLE{e91-a_9_2419,
author={Yuichi TANJI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method},
year={2008},
volume={E91-A},
number={9},
pages={2419-2425},
abstract={The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.},
keywords={},
doi={10.1093/ietfec/e91-a.9.2419},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Sparse and Passive Reduced-Order Interconnect Modeling by Eigenspace Method
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2419
EP - 2425
AU - Yuichi TANJI
PY - 2008
DO - 10.1093/ietfec/e91-a.9.2419
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2008
AB - The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.
ER -
English
