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GAN-based statistical modeling with adaptive schemes for surface defect inspection of IC metal packages

Author

Listed:
  • Zhenshuang Wu

    (Guangdong University of Technology
    Guangdong University of Technology
    Institute of Corrosion Science and Technology)

  • Nian Cai

    (Guangdong University of Technology
    Guangdong University of Technology
    Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co.,Ltd.)

  • Kaiqiong Chen

    (Guangdong University of Technology
    Guangdong University of Technology)

  • Hao Xia

    (China Electronic Product Reliability and Environment Testing Research Institute)

  • Shuai Zhou

    (China Electronic Product Reliability and Environment Testing Research Institute)

  • Han Wang

    (Guangdong University of Technology)

Abstract

Metal packaging is an alternative technology to guarantee the environmental resistance and the performance reliability of ICs. Surface defect inspection of IC metal packages is an indispensable process during manufacturing. Here, a statistical modeling framework is proposed based on a GAN for surface defect inspection of IC metal packages, which involves several adaptive schemes. To the best of our knowledge, we first introduce the GAN to establish a machine vision based method for surface defect inspection of IC metal packages. IC metal package images are automatically acquired by an AOI system and employed for inspection via the proposed framework. To tackle the problem of imbalanced data in real industries, the framework only utilizes qualified samples to train the GAN template generator, which can characterize the intrinsic pattern of qualified samples. Then, a weight mask scheme is proposed to suppress the interference pixels in the difference image corresponding to qualified samples. Next, an adaptive thresholding scheme is proposed to adaptively determine an appropriate threshold for each inspected sample. Finally, an image patch-based defect evaluation scheme is designed to local-to-global evaluate the surface qualities of IC metal packages. Comparison experiments indicate that the proposed framework achieves better inspection performance in terms of 3.16% error rate and 0.89% mission rate at a reasonable inspection time of 119.86 ms per sample, which is superior to some existing deep learning based inspection methods for surface defect inspection of IC metal packages.

Suggested Citation

  • Zhenshuang Wu & Nian Cai & Kaiqiong Chen & Hao Xia & Shuai Zhou & Han Wang, 2024. "GAN-based statistical modeling with adaptive schemes for surface defect inspection of IC metal packages," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1811-1824, April.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02146-9
    DOI: 10.1007/s10845-023-02146-9
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    References listed on IDEAS

    as
    1. Ssu-Han Chen & Der-Baau Perng, 2016. "Automatic optical inspection system for IC molding surface," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 915-926, October.
    2. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
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