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    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 期刊論文 >  Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/36632

    Title: Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing
    Authors: Shao-Chung Hsu;Chen-Fu Chien
    教師: 簡禎富
    Date: 2007
    Publisher: Elsevier
    Relation: International Journal of Production Economics, Volume 107, Issue 1, May 2007, Pages 88-103
    Keywords: Spatial randomness test
    Quality engineering
    Semiconductor manufacturing
    Neural networks
    Data mining
    Yield improvement
    Wafer bin map
    Abstract: © 2007 Elsevier - 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.
    URI: http://www.elsevier.com/
    Appears in Collections:[工業工程與工程管理學系] 期刊論文

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