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A chip inspection system based on a multiscale subarea attention network

Author

Listed:
  • Yun Hou

    (Southwest China Institute of Electronic Technology)

  • Hong Fan

    (Sichuan Polytechnic University)

  • Ying Chen

    (Southwest China Institute of Electronic Technology)

  • Guangshuai Liu

    (Southwest Jiaotong University)

Abstract

Cavities in a weld seriously affect the airtightness of the chip, which makes chip inspection a crucial step in intelligent manufacturing. In recent years, deep learning-based defect inspection models have shown significant advantages in reducing human errors. However, due to the scarcity of defective data, deep learning-based models are susceptible to overfitting. Moreover, the multiscale and uneven grayscale distribution of cavities further compound the challenges faced by these models. To address these issues, we develop a chip inspection system based on a multiscale subarea attention network (MSANet) for cavity defect detection. In the system, the segment anything model is embedded to interactively segment the weld. Furthermore, to circumvent the overfitting problem, a large-scale cavity dataset is built by splitting the segmented weld into multiple patches. Notably, a novel MSANet is proposed to precisely segment the varying cavities, and a source-to-destination Dijkstra algorithm is designed to assess the chip quality. The experimental results demonstrate that our chip inspection system achieves a 99.24% F1-score and 99.26% AUC.

Suggested Citation

  • Yun Hou & Hong Fan & Ying Chen & Guangshuai Liu, 2025. "A chip inspection system based on a multiscale subarea attention network," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 4039-4053, August.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02441-z
    DOI: 10.1007/s10845-024-02441-z
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    References listed on IDEAS

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    1. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Maike Lorena Stern & Martin Schellenberger, 2021. "Fully convolutional networks for chip-wise defect detection employing photoluminescence images," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 113-126, January.
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