Neural network approach to topological via-minimization problems

Nobuo Funabiki, Yoshiyasu Takefuji

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Topological via-minimization (TVM) algorithms in two-layer channels based on the artificial neural network model are presented in this paper. TVM problems require not only assigning wires or nets between terminals without an intersection to one of two layers, but also a minimization of the number of vias, which are the single contacts of nets between two layers. The goal of our algorithms is to embed the maximum number of nets without an intersection. Two types of TVM problems are examined: split rectangular TVM (RTVM) problems and split circular TVM (CTVM) problems. Our algorithms require 3 n processing elements for the n-net split RTVM problems, and 5 n processing elements for the n-net split CTVM problems. The algorithms were verified by solving seven problems with 20 to 80 nets. The algorithms can be easily extended for more-than-two-layer problems.

Original languageEnglish
Pages (from-to)770-779
Number of pages10
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume12
Issue number6
DOIs
Publication statusPublished - 1993 Jun
Externally publishedYes

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Neural networks
Processing
Wire

ASJC Scopus subject areas

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

Cite this

Neural network approach to topological via-minimization problems. / Funabiki, Nobuo; Takefuji, Yoshiyasu.

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 12, No. 6, 06.1993, p. 770-779.

Research output: Contribution to journalArticle

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