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A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

Author

Listed:
  • Youngju Kim

    (Yonsei University)

  • Hoyeop Lee

    (Yonsei University)

  • Chang Ouk Kim

    (Yonsei University)

Abstract

In the semiconductor manufacturing field, few studies on fault detection (FD) models have considered process drift due to incomplete maintenance. Process drift refers to the shift in sensor measurements over time due to tool aging, and it leads to defective production when it is severe. Tool maintenance is conducted regularly to prevent defects. However, when it is performed improperly, tool aging accelerates, and the drift increases. In this paper, we propose an FD model robust to process drift by modeling process drift with a variational autoencoder (VAE). Because process drift is characterized by time-varying information, the proposed model encodes some time-varying information through separate hidden layers. By adopting a strategy that combines information separately encoded in a feature vector, the proposed model successfully models process drift. With actual chemical vapor deposition process data, we were able to generate many virtual datasets that incorporate process drift with various drift characteristics, such as patterns, degrees, and speeds. The proposed model outperformed four comparison FD methods on these datasets.

Suggested Citation

  • Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01810-2
    DOI: 10.1007/s10845-021-01810-2
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    References listed on IDEAS

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    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    3. Ki Bum Lee & Chang Ouk Kim, 2020. "Recurrent feature-incorporated convolutional neural network for virtual metrology of the chemical mechanical planarization process," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 73-86, January.
    4. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    5. A. Khatab, 2018. "Maintenance optimization in failure-prone systems under imperfect preventive maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 707-717, March.
    Full references (including those not matched with items on IDEAS)

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