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A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing

Author

Listed:
  • Yi Zhang

    (Tsinghua University
    Naval Research Academy)

  • Peng Peng

    (Tsinghua University)

  • Chongdang Liu

    (Tsinghua University)

  • Yanyan Xu

    (Tsinghua University
    Unit 94926)

  • Heming Zhang

    (Tsinghua University)

Abstract

Fault detection is one of the most important research topics to guarantee safe operation and product quality consistency especially in the batch process of semiconductor manufacturing. However, the imbalanced fault data bring great challenges to extract the high nonlinearity and inherently time-varying dynamics of the batch process. Motivated by these, we propose a sequential oversampling discrimination approach for imbalanced batch process fault detection. Especially, different from the traditional oversampling methods, which extract temporal features from the whole process, we transform a whole batch sequence into multiple fixed-length sequences each batch by a sliding window, to extract the robust time-varying dynamics features. Then, an oversampling neural network is performed to balance both sequences of minority and majority classes. The needed sequences of the minority class are generated by an improved combination model of variational auto-encoder and generative adversarial network. Finally, a simplified sequential neural network is learned by the balanced-class sequences to perform the discrimination. We conduct extensive experiments based on two datasets of semiconductor manufacturing. One is a benchmark dataset and the other is a dataset from a real production line. The results achieved significant improvement, compared with other state-of-art fault detection methods and oversampling techniques.

Suggested Citation

  • Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01716-5
    DOI: 10.1007/s10845-020-01716-5
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    References listed on IDEAS

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    1. Maroua Said & Khaoula ben Abdellafou & Okba Taouali, 2020. "Machine learning technique for data-driven fault detection of nonlinear processes," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 865-884, April.
    2. Qifa Xu & Shixiang Lu & Weiyin Jia & Cuixia Jiang, 2020. "Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1467-1481, August.
    3. Qianhui Wu & Keqin Ding & Biqing Huang, 2020. "Approach for fault prognosis using recurrent neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1621-1633, October.
    4. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    5. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
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