On projection-based algorithms for model-order reduction of interconnects

Janet Meiling Wang, Chia Chi Chu, Qingjian Yu, Ernest S. Kuh

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1563-1585
Number of pages23
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Issue number11
StatePublished - Nov 2002


  • Coefficient matching
  • Congruence transform
  • Generalized orthonormal basis function
  • Grammian
  • Interconnect
  • Model order reduction
  • Multipoint moment matching
  • Orthonormal polynomials
  • Projection-based algorithms

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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