Browse Bar: Browse by Author | Browse by Category | Browse by Citation | Advanced Search
Author(s): Chien, C.F. | Hsu, S.C. |
Year: 2007
Citation: INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS Volume: 107 Issue: 1 Pages: 88-103
Abstract: Semiconductor manufacturing involves lengthy and complex processes, and hence is capital intensive. Companies compete with each other by continuously employing new technologies, increasing yield, and reducing costs. Yield improvement is increasingly important as advanced fabrication technologies are complicated and interrelated. In particular, wafer bin maps (WBM) that present specific failure patterns provide crucial information to track the process problems in semiconductor manufacturing, yet most fabrication facility (fabs) rely on experienced engineers judgments of the map patterns through eye-ball analysis. Thus, existing studies are subjective, time consuming, and are also restricted by the capability of human recognition. This study proposes a hybrid data mining approach that integrates spatial statistics and adaptive resonance theory neural networks to quickly extract patterns from WBM and associate with manufacturing defects. An empirical study of WBM clustering was conducted in a fab for validation. The results showed practical viability of the proposed approach and now an expert system embedded with the developed algorithm has been implemented in a fab in Taiwan. This study concludes with a discussion on further research.
Topics:
Machine Learning,
Applications:
Industrial Control,
Models:
ART 1,
The ART of adaptive pattern recognition by a self organizing neural network
The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to ... Article Details