Comparative study of convolution and order reduction techniques for blackbox macromodeling using scattering parameters

Jose E Schutt-Aine, Patrick Goh, Yidnekachew Mekonnen, Jilin Tan, Feras Al-Hawari, Ping Liu, Wenliang Dai

Research output: Contribution to journalArticle

Abstract

In this paper, a fast convolution method using scattering parameters is presented for the macromodeling of blackbox multiport networks. The method is compared to model-order reduction passive macromodeling techniques in terms of robustness and computational efficiency. When scattering parameters are used as the transfer functions, convolution calculations can be accelerated to achieve superior performance and the resulting procedure leads to a robust, accurate, and efficient macromodel generation scheme. This paper examines the formulation of the convolution method. Model-order reduction techniques are reviewed and benchmark comparisons are performed.

Original languageEnglish (US)
Article number6021337
Pages (from-to)1642-1650
Number of pages9
JournalIEEE Transactions on Components, Packaging and Manufacturing Technology
Volume1
Issue number10
DOIs
StatePublished - Oct 1 2011

Keywords

  • Blackbox
  • causality
  • convolution
  • macromodel
  • passivity
  • scattering parameters
  • vector fitting

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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