The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article décrit une approche hybride de réseau neuronal auto-organisé dirigée par la force pour le placement de cartes de circuits imprimés (PCB) en tenant compte de la compatibilité électromagnétique (CEM). Dans la plupart des algorithmes conventionnels de placement automatique des PCB, le seul facteur pris en compte dans la fonction objectif est la longueur nette totale minimisée. Cependant, pour les circuits imprimés haute vitesse et haute densité d'aujourd'hui, la conformité CEM ne peut pas être atteinte par un tel objectif unique. Pour résoudre ce problème, l'algorithme présenté prend en compte la CEM, outre le chevauchement des composants et la longueur nette totale minimisée. Ces facteurs sont optimisés au moyen d’une carte auto-organisatrice adaptée. La comparaison des résultats de placement simulés ainsi que des mesures réelles avec des logiciels commerciaux confirme l'efficacité de la méthode proposée.
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Teck Lin ANG, Yuji TARUI, Takashi SAKUSABE, Takehiro TAKAHASHI, Noboru SCHIBUYA, "A Hybrid Force-Directed Self-Organizing Neural Network Approach to Automatic Printed Circuit Board Component Placement with EMC Consideration" in IEICE TRANSACTIONS on Communications,
vol. E85-B, no. 9, pp. 1797-1805, September 2002, doi: .
Abstract: This paper describes a hybrid force-directed self-organizing neural network approach to printed circuit board (PCB) placement with consideration of electromagnetic compatibility (EMC). In most of the conventional PCB automatic placement algorithms, the only factor considered in the objective function is minimized total net length. However, for today's high speed and high density PCB, EMC compliance cannot be met by such single objective. To tackle this problem, the presented algorithm takes EMC into consideration, besides component overlap and minimized total net length. These factors are optimized by means of an adapted self-organizing map. Comparison of simulated placement results as well as actual measurements with commercial softwares confirms the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_9_1797/_p
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@ARTICLE{e85-b_9_1797,
author={Teck Lin ANG, Yuji TARUI, Takashi SAKUSABE, Takehiro TAKAHASHI, Noboru SCHIBUYA, },
journal={IEICE TRANSACTIONS on Communications},
title={A Hybrid Force-Directed Self-Organizing Neural Network Approach to Automatic Printed Circuit Board Component Placement with EMC Consideration},
year={2002},
volume={E85-B},
number={9},
pages={1797-1805},
abstract={This paper describes a hybrid force-directed self-organizing neural network approach to printed circuit board (PCB) placement with consideration of electromagnetic compatibility (EMC). In most of the conventional PCB automatic placement algorithms, the only factor considered in the objective function is minimized total net length. However, for today's high speed and high density PCB, EMC compliance cannot be met by such single objective. To tackle this problem, the presented algorithm takes EMC into consideration, besides component overlap and minimized total net length. These factors are optimized by means of an adapted self-organizing map. Comparison of simulated placement results as well as actual measurements with commercial softwares confirms the effectiveness of the proposed method.},
keywords={},
doi={},
ISSN={},
month={September},}
Copier
TY - JOUR
TI - A Hybrid Force-Directed Self-Organizing Neural Network Approach to Automatic Printed Circuit Board Component Placement with EMC Consideration
T2 - IEICE TRANSACTIONS on Communications
SP - 1797
EP - 1805
AU - Teck Lin ANG
AU - Yuji TARUI
AU - Takashi SAKUSABE
AU - Takehiro TAKAHASHI
AU - Noboru SCHIBUYA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2002
AB - This paper describes a hybrid force-directed self-organizing neural network approach to printed circuit board (PCB) placement with consideration of electromagnetic compatibility (EMC). In most of the conventional PCB automatic placement algorithms, the only factor considered in the objective function is minimized total net length. However, for today's high speed and high density PCB, EMC compliance cannot be met by such single objective. To tackle this problem, the presented algorithm takes EMC into consideration, besides component overlap and minimized total net length. These factors are optimized by means of an adapted self-organizing map. Comparison of simulated placement results as well as actual measurements with commercial softwares confirms the effectiveness of the proposed method.
ER -