A hybrid integral equation and neural network approach for fast extraction of frequency dependent parameters of multiconductor transmission lines

G. Pan, P. Piel, Barry Kent Gilbert

Research output: Contribution to journalArticle

Abstract

Multiconductor transmission lines (MTL) have been modeled by the distributed parameters R, L, C, and G in many commercial CAD packages, where most of the parameters are assumed to be frequency independent or at most R ∝ √f. At gigahertz frequencies, such assumptions may introduce significantly large errors in the waveform simulation and timing. In this article, we present a new and fast technique based on a combination of neural network techniques and the integral equation method (IEM) to evaluate frequency dependences accurately, while dramatically reducing the computation time.

Original languageEnglish (US)
Pages (from-to)37-50
Number of pages14
JournalInternational Journal of RF and Microwave Computer-Aided Engineering
Volume12
Issue number1
DOIs
StatePublished - Jan 2002

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Integral equations
Electric lines
Computer aided design
Neural networks

Keywords

  • Integral equation
  • Neural networks
  • Transmission lines
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

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AB - Multiconductor transmission lines (MTL) have been modeled by the distributed parameters R, L, C, and G in many commercial CAD packages, where most of the parameters are assumed to be frequency independent or at most R ∝ √f. At gigahertz frequencies, such assumptions may introduce significantly large errors in the waveform simulation and timing. In this article, we present a new and fast technique based on a combination of neural network techniques and the integral equation method (IEM) to evaluate frequency dependences accurately, while dramatically reducing the computation time.

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