Decision Support for IC Molding Parameter Settings Using Grey Relational Analysis and Neural Network
- https://doi.org/10.2991/jcis.2006.140How to use a DOI?
- multiple quality characteristics, Taguchi method, grey relational analysis, neural network
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/jcis.2006.140 ID - Chiang2006/10 ER -