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

Chen Luo (), Tingxiao Fan, Yan Xia, Yijun Zhou, Lei Jia and Baocheng Hui
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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

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 10, 209-219

Abstract: 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.

Keywords: Conductive particle inspection; Deep learning; Convolutional neural network; TFT-LCD; Intelligent manufacturing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02241-x

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