TY - JOUR
T1 - On projection-based algorithms for model-order reduction of interconnects
AU - Wang, Janet Meiling
AU - Chu, Chia Chi
AU - Yu, Qingjian
AU - Kuh, Ernest S.
N1 - Funding Information:
Manuscript received January 17, 2001; revised November 11, 2001. This work was supported by Semiconductor Research Corporation (SRC) under Contract 866.001. The work of C.-C. Chu was supported in part by the National Science Council, Taiwan, R.O.C. under Grant NSC37032F. This paper was recommended by Associate Editor K. Thulasiraman. J. M. Wang is with the Electrical Computing Engineering Department, University of Arizona, Tucson, AR 85721 USA. C.-C. Chu is with the Department of Electrical Engineering, Chang Gung University, Tao-Yuan 333, Taiwan, R.O.C. Q. Yu is with Celestry Design Technologies, Inc., San Jose, CA 95134 USA ([email protected]). E. S. Kuh is with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA. Publisher Item Identifier 10.1109/TCSI.2002.804542.
PY - 2002/11
Y1 - 2002/11
N2 - Model-order reduction is a key technique to do fast simulation of interconnect networks. Among many model-order reduction algorithms, those based on projection methods work quite well. In this paper, we review the projection-based algorithms in two categories. The first one is the coefficient matching algorithms. We generalize the Krylov subspace method on moment matching at a single point, to multipoint moment-matching methods with matching points located anywhere in the closed right-hand side (RHS). of the complex plane, and we provide algorithms matching the coefficients of series expansion-based on orthonormal polynomials and generalized orthonormal basis functions in Hilbert and Hardy space. The second category belongs to the grammian-based algorithms, where we provide efficient algorithm for the computation of grammians and new approximate grammian-based approaches. We summarize some important properties of projection-based algorithms so that they may be used more flexibly.
AB - Model-order reduction is a key technique to do fast simulation of interconnect networks. Among many model-order reduction algorithms, those based on projection methods work quite well. In this paper, we review the projection-based algorithms in two categories. The first one is the coefficient matching algorithms. We generalize the Krylov subspace method on moment matching at a single point, to multipoint moment-matching methods with matching points located anywhere in the closed right-hand side (RHS). of the complex plane, and we provide algorithms matching the coefficients of series expansion-based on orthonormal polynomials and generalized orthonormal basis functions in Hilbert and Hardy space. The second category belongs to the grammian-based algorithms, where we provide efficient algorithm for the computation of grammians and new approximate grammian-based approaches. We summarize some important properties of projection-based algorithms so that they may be used more flexibly.
KW - Coefficient matching
KW - Congruence transform
KW - Generalized orthonormal basis function
KW - Grammian
KW - Interconnect
KW - Model order reduction
KW - Multipoint moment matching
KW - Orthonormal polynomials
KW - Projection-based algorithms
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U2 - 10.1109/TCSI.2002.804542
DO - 10.1109/TCSI.2002.804542
M3 - Article
AN - SCOPUS:0036857202
SN - 1057-7122
VL - 49
SP - 1563
EP - 1585
JO - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
JF - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
IS - 11
ER -