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  • Mining batch activation rules from event logs

    • Authors: Martin, Niels;  Advisor: -;  Participants: Solti, Andreas; Mendling, Jan; Depaire, Benoit; Caris, An (2020)

  • Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual ba...
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  • Data Science for Delamination Prognosis and Online Batch Learning in Semiconductor Assembly Process

    • Authors: Hung, Shao-Yen;  Advisor: -;  Participants: Lee, Chia-Yen; Lin, Yung-Lun (2020)

  • The transformation of wafers into chips is a complex manufacturing process involving literally thousands of equipment parameters. Delamination, a leading cause of defective products, can occur between die and epoxy molding compound (EMC), epoxy and substrate, lead frame and EMC, etc. Troubleshooting is generally on a case-by-case basis and is both time-consuming and labor intensive. We propose a three-phase data science framework for process prognosis and prediction. The first phase is for data preprocessing. The second phase uses LASSO regression and stepwise regression to identify the key variables affecting delamination. The third phase develops backpropagation neural network (BPNN), support vector regression (SVR), partial least squares (PLS), and gradient boosting machine (GBM)...
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