Data mining for improving the solder bumping process in the semiconductor packaging industry

Authors

  • Chen-fu Chien,

    Corresponding author
    1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC
    • Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, 101 Sec. 2 Kuang Fu Road, Hsinchu 30043, Taiwan, ROC.
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  • Huan-chung Li,

    1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC
    2. Department of Industrial Engineering Management, Chin Min Institute Technology, Miao-Li, Taiwan, ROC
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  • Angus Jeang

    1. Department of Industrial Engineering, Feng Chia University, Taichung, Taiwan, ROC
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Abstract

Modern semiconductor manufacturing is very complex and expensive. Maintaining high quality and yield enhancement have been recognized as important factors to build core competences for semiconductor manufacturing companies. Data mining can find potentially useful information from huge databases. This paper proposes a data-mining framework based on decision-tree induction for improving the yield of the solder bumping process in which the various (physical and chemical) input variables that affect the bumping process exhibit highly complex interactions. We conducted an empirical study in a semiconductor fabrication facility in Taiwan to validate this approach. The results show that the proposed approach can effectively derive the causal relationships among controllable input process factors and the target class to enhance the yield. Copyright © 2007 John Wiley & Sons, Ltd.

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