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Deep learning-based conductive particle inspection for TFT-LCDs inspired by parametric space envelope

Author

Listed:
  • Chen Luo

    (Southeast University)

  • Tingxiao Fan

    (Southeast University)

  • Yan Xia

    (Southeast University)

  • Yijun Zhou

    (Southeast University)

  • Lei Jia

    (Shangshi Finevision Co., Ltd)

  • Baocheng Hui

    (Citigroup Centre)

Abstract

The inspection of conductive particles after Anisotropic Conductive Film bonding is a crucial step in TFT-LCD manufacturing for quality assurance. Manual inspection under microscope is labor-intensive, time-consuming and error prone. Automatic inspection methods have been proposed by researchers including deep learning methods. However, inspection results are case dependent and existing deep learning-based methods heavily rely on large training dataset which is not given in many real applications. This is because the data available for analysis is limited on the manufacturing lines. To take on this challenge, this paper proposes a novel deep learning method based on modified Mask R-CNN algorithm which performs pixel-level segmentation to detect conductive particles. Under the proposed method, training dataset is augmented by applying novel parametric space envelope technique through a label-preserving transformation. This helps address small sample size prediction problem as well as class imbalance issue within the training dataset. Experimental results show significant improvement over existing methods under real-world constraint of limited training data (i.e., 99.25% overall particle detection accuracy compared with ~ 90% from existing template matching based auto-inspection method). The developed method provides industries an intelligent way to inspect conductive particle in TFT-LCD manufacturing.

Suggested Citation

  • Chen Luo & Tingxiao Fan & Yan Xia & Yijun Zhou & Lei Jia & Baocheng Hui, 2025. "Deep learning-based conductive particle inspection for TFT-LCDs inspired by parametric space envelope," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 209-219, January.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02241-x
    DOI: 10.1007/s10845-023-02241-x
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    References listed on IDEAS

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    1. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    2. Xiongping Yue & Dong Mu & Chao Wang & Huanyu Ren & Pezhman Ghadimi, 2023. "Topological structure and COVID-19 related risk propagation in TFT-LCD supply networks," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2758-2778, April.
    3. Myeongso Kim & Minyoung Lee & Minjeong An & Hongchul Lee, 2020. "Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1165-1174, June.
    4. Eryun Liu & Kangping Chen & Zhiyu Xiang & Jun Zhang, 2020. "Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1037-1049, April.
    5. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    6. Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
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