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Automatic inspection and strategy for surface defects in the PI coating process of TFT-LCD panels

机译：TFT-LCD面板的PI涂层工艺中的表面缺陷自动检查和处理策略

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摘要

Purpose-The purpose of this paper is to apply an on-line automatic inspection and measurement of surface defect of thin-film transistor liquid-crystal display (TFT-LCD) panels in the polyimide coating process with a modified template matching method and back propagation neural network classification method. Design/methodology/approach-By using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT-LCD cell defects can be obtained. Findings-With template match and neural network classification in the database of the system, the program judges the kinds of the target defects characteristics, finds out the central position of cell defect, and analyzes cell defects. Research limitations/implications-The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method and strategy, using unsophisticated and economical equipment, is also verified. The proposed method provides highly accurate results with a low-error rate. Practical implications-In terms of sample training, the principles of artificial neural network were used to train the sample detection rate. In sample analysis, character weight was implemented to filter the noise so as to enhance discrimination and reduce detection. Originality/value-The paper describes how pre-inspection image processing was utilized in collaboration with the system to excel the inspection efficiency of present machines as well as for reducing system misjudgment. In addition, the measure for improving cell defect inspection can be applied to production line with multi-defects to inspect and improve six defects simultaneously, which improves the system stability greatly.
机译：目的-本文的目的是通过改进的模板匹配方法和反向传播，对聚酰亚胺涂层工艺中的薄膜晶体管液晶显示器（TFT-LCD）面板的表面缺陷进行在线自动检查和测量神经网络分类方法。设计/方法/方法-通过使用搜索，分析和识别图像处理方法的技术，可以获得TFT-LCD单元缺陷的目标图案图像。结果-利用系统数据库中的模板匹配和神经网络分类，程序可以判断目标缺陷特征的种类，找出细胞缺陷的中心位置，并分析细胞缺陷。研究局限/意义-与以前的系统相比，识别速度更快，并且系统变得更加灵活。还验证了所提出的使用简单且经济的设备的方法和策略。所提出的方法提供了具有低错误率的高精度结果。实际意义-在样本训练方面，使用人工神经网络原理来训练样本检测率。在样本分析中，使用字符权重来过滤噪声，以增强区分度并减少检测。独创性/价值-本文介绍了如何与系统协同使用预检图像处理，以提高当前机器的检查效率并减少系统误判。另外，可以将改进的电池缺陷检查措施应用于具有多个缺陷的生产线，以同时检查和改善六个缺陷，从而大大提高了系统的稳定性。

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  • 来源
    《Assembly Automation》 |2011年第3期|p.244-250|共7页
  • 作者

    Chern-Sheng Lin; Jung Kuo; Chi-Chin Lin; Yun-Long Lay; Hung-fung Shei;

  • 作者单位

    Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan;

    Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan;

    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;

    Department of Electronic Engineering, National Chinyi University of Technology, Taiping City, Taiwan;

    Department of Mechanical Engineering, China Institute of Technology, Taipei, Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    automated test equipment; assembly; inspection and testing; flaw detection; sensor review; coating processes;

    机译：自动化测试设备;部件;检验和测试;探伤;传感器审查;涂层工艺;

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