Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach View Full Text


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Article Info

DATE

2003-04

AUTHORS

Chao-Ton Su, Tai-Lin Chiang

ABSTRACT

A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability. More... »

PAGES

229-238

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1022959631926

DOI

http://dx.doi.org/10.1023/a:1022959631926

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009401153


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