Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing

Eryun Liu (), Kangping Chen (), Zhiyu Xiang () and Jun Zhang ()
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Eryun Liu: Zhejiang University
Kangping Chen: Zhejiang University
Zhiyu Xiang: Zhejiang University
Jun Zhang: Tencent AI Healthcare

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 15, 1037-1049

Abstract: Abstract The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since the number of high-quality conductive particles is a key indicator of ACF bonding quality. However, manual inspection under microscope is a time-consuming, tedious, and error-prone. Therefore, there is an urgent demand in industry for the automatic conductive particle inspection system. It is challenging for automatic conductive particle quality inspection due to the existence of complex background noise and diversified particle appearance, including shape, size, clustering and overlapping, etc. As a result, it lacks an effective automatic detection method to handle all the complex particle patterns. In this paper, we propose a U-shaped deep residual neural network (i.e., U-ResNet), which can learn features of particle from massively labeled data. The experimental results show that the proposed method achieves high detection accuracy and recall rate, which exceedingly outperforms the previous work. Also, our system is very efficient and can work in real time.

Keywords: Conductive particles; ACF bonding; TFT-LCD; Deep convolutional network; U-ResNet (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-019-01494-9

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