A Neural-based Algorithm for Topological Via-minimization Problem

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概要

• 論文の詳細を見る

• Abstract In this paper we present a neural-based algorithm for topological via-minimization (TVM) problem in two-layer channels. TVM problem requires not only assigning wires or nets between terminals without an intersection to one of the two layers, but also a minimization of the number of vias, which are the single contacts of nets between two layers. The proposed algorithm which is designed to embed the maximum numbers of nets without an intersection, uses gradient ascent learning of the coefficients to help the Hopfield network escape from local minima and find a global minimum. The proposed algorithm is applied to the split rectangular TVM (RTVM) problem and simulations are performed. The experimental results show that the proposed algorithm generates much better solutions than other existing algorithms for this problem.

• 社団法人 電気学会の論文
• 2004-06-01

著者

• TANG Zheng

  Faculty of Engineering, Toyama University

• TAMURA Hiroki

  Faculty of Engineering, Toyama University

• Wang Rong

  The Faculty Of Engineering Fukui University

• Tamura H

  Univ. Of Miyazaki Miyazaki‐shi Jpn

• Tamura Hiroki

  Faculty Of Engineering Toyama University

• Tang Zheng

  Faculty Of Engineering Miyazaki University

• Wang R

  Faculty Of Engineering Fukui University

• CAO Qiping

  Tateyama Institute of System

• Cao Q

  Tateyama Systems Institute

• XU Xinshun

  Faculty of Engineering, Toyama University

• WANG Ronglong

  Faculty of Engineering, Fukui University

• Wang Ronglong

  Faculty Of Engineering Fukui University

• Tamura H

  Faculty Of Engineering Toyama University

• Xu Xinshun

  Faculty Of Engineering Toyama University

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