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Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling

Author

Listed:
  • Shouhong Chen

    (Guilin University of Electronic Technology)

  • Zhentao Huang

    (Guilin University of Electronic Technology)

  • Tao Wang

    (Guilin University of Electronic Technology)

  • Xingna Hou

    (Guilin University of Electronic Technology
    Guilin University of Electronic Technology)

  • Jun Ma

    (Guilin University of Electronic Technology)

Abstract

Wafers are products in semiconductor manufacturing and serve as the foundation for producing semiconductor chips. During the wafer testing stage, functional and electrical parameters are examined to identify defects in chip design and fabrication. The wafer map is the result of the wafer testing process. Analyzing and classifying defective information on the wafer map aids in defect source identification and optimization of the wafer production process. Deep learning has been employed for defect detection on wafer maps because of its superior image processing capabilities. Nevertheless, as semiconductor chip design integration and wafer size increase, more complex types of wafer defects tend to emerge in the production process, and the size, shape, and distribution of wafer defects can affect the final classification outcome. Accordingly, this study proposes a deep learning model, called ESPP-Net (Attention Spatial Pyramid Pooling Network), that combines a deep convolutional neural network and attention space pyramid pooling to recognize and classify single and mixed-type defect wafer maps. We evaluated our model on both the mixed-type dataset Mixed38WM and the single-type dataset WM-811K and compared it with state-of-the-art deep learning models. Experimental results show that our proposed model outperformed the preexisting models and demonstrated superior classification performance.

Suggested Citation

  • Shouhong Chen & Zhentao Huang & Tao Wang & Xingna Hou & Jun Ma, 2025. "Wafer map defect recognition based on multi-scale feature fusion and attention spatial pyramid pooling," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 271-284, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02231-z
    DOI: 10.1007/s10845-023-02231-z
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    References listed on IDEAS

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    1. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    2. Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
    Full references (including those not matched with items on IDEAS)

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