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In this study an automatic optical inspection system is presented to evaluate the fracture and deformation status of conducting particles of anisotropic conductive film in the TFT-LCD assembly process. The amount of deformation and quantity of conducting particles in the test pattern can be automatically evaluated by image analysis. A specific operation is carried out in the image processing method, and the calculation of the image gradient operator is used to produce a preferable contrast between the processed particle image and the background. The thinning processing method is applied for information reduction and information creation. An amount of samples are taken with a target template for synchronous multiple-comparison, and the optimal threshold of the binary image is obtained. This study utilizes the assistance of image processing technology to inspect the fracture conditions of anisotropic conductive film in the TFT-LCD assembly process. This system can decrease the defection rate of products, obtain over 90% recognition accuracy even in noisy environments, and will be verified in an automatic production line.

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PurposeThe 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/approachBy using the technique of searching, analyzing, and recognizing image processing methods, the target pattern image of TFT‐LCD cell defects can be obtained.FindingsWith 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/implicationsThe 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 implicationsIn 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/valueThe 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.