Decision Support for IC Molding Parameter Settings Using Grey Relational Analysis and Neural Network

DOI

10.2991/jcis.2006.140How to use a DOI?

Keywords

multiple quality characteristics, Taguchi method, grey relational analysis, neural network

Abstract

In order to be competitive in the semiconductor manufacturing industry, quality improvement and yield enhancement have received increasing attention. The research focuses on the molding process of Integrated Circuit (IC) assembly. The defects often occurred in molding process include hole, vein, crack, and floss. In order to raise the yield of molding process, the study applies the Taguchi method combined with grey relational analysis to find out the most appropriate molding parameters with multiple quality characteristics. The study further adopts a back-propagation neural network to estimate the optimal process parameters. Results show that the proposed approach can improve the quality of the molding process.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yu-Min Chiang
AU  - Chung-Hsien Chou
AU  - Yung-Yuan Chuang
PY  - 2006/10
DA  - 2006/10
TI  - Decision Support for IC Molding Parameter Settings Using Grey Relational Analysis and Neural Network
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.140
DO  - 10.2991/jcis.2006.140
ID  - Chiang2006/10
ER  -