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Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm

Author

Listed:
  • Tae San Kim

    (Yonsei University
    SK Telecom)

  • Jong Wook Lee

    (Yonsei University
    LG Electronics)

  • Won Kyung Lee

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process. The rapid advancement of the integrated circuit technology has recently led to more frequent occurrences of mixed-type defect patterns, wherein two or more defect patterns simultaneously occur in a wafer bin map. The detection of these mixed patterns is more difficult than that of single patterns. To detect these mixed patterns, binary relevance approaches based on convolutional neural networks have been proposed. However, as the manufacturing process has been advanced and integrated, various failure types are newly detected, thus the number of single models can be continuously increased following the diversification of defect types. Therefore, we propose an effective framework for detecting mixed-type patterns in which a simple single model, called the single shot detector, is employed. By applying the proposed model to the WM-811K dataset, we show that our framework outperforms existing CNN-based methods and also provides defect location information.

Suggested Citation

  • Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01755-6
    DOI: 10.1007/s10845-021-01755-6
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    References listed on IDEAS

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    1. Francisco G. Bulnes & Ruben Usamentiaga & Daniel F. Garcia & J. Molleda, 2016. "An efficient method for defect detection during the manufacturing of web materials," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 431-445, April.
    2. Haiyong Chen & Yue Pang & Qidi Hu & Kun Liu, 2020. "Solar cell surface defect inspection based on multispectral convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 453-468, February.
    3. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    4. Jinho Kim & Youngmin Lee & Heeyoung Kim, 2018. "Detection and clustering of mixed-type defect patterns in wafer bin maps," IISE Transactions, Taylor & Francis Journals, vol. 50(2), pages 99-111, February.
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    Cited by:

    1. Shijie Wang & Haiyong Chen & Kun Liu & Ying Zhou & Huichuan Feng, 2023. "Meta-FSDet: a meta-learning based detector for few-shot defects of photovoltaic modules," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3413-3427, December.

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