Abstract
Defects on thin film transistor liquid crystal display (TFT-LCD) panel could be divided into either macro- or microdefects, depending on if they are easy to be detected by the naked eye or not. There have been abundant studies discussing the identification of macrodefects but very few on microones. This study proposed a multicategory classification model using a convolutional neural network model to work with automatic optical inspection (AOI) for identifying defective pixels on the TFT-LCD panel. Since the number of nondefective pixels outnumbered the defective ones, there exists a very serious class-imbalanced problem. To deal with that, this study designed a special training strategy that worked with data augmentation to increase the effectiveness of the proposed model. Actual panel images provided by a mobile manufacturer in Taiwan are used to demonstrate the efficiency and effectiveness of the proposed approach. After validation, the model constructed by this study had 98.9% total prediction accuracy and excellent specificity and sensitivity. The model could finish the detection and classification process automatically to replace the human inspection.
Highlights
Defects on thin film transistor-liquid crystal displays (TFTLCD) can be divided into macrodefects and microdefects [1].e main difference between them is whether they can be detected by the naked eye or not
With the recent advanced high-resolution camera and data processing technologies, automatic optical inspection (AOI) became an alternative. is paper aimed at constructing a multicategory classification model based on deep learning that could be used to improve the efficiency of model construction to identify defective pixels in TFTLCD panels while maintaining high classification accuracy
Due to the fact that micodefects, especially defective pixels, were still allowed to exist in top-graded thin film transistor liquid crystal display (TFT-LCD) panels supplied by TFT-LCD manufacturers, sorting and categorizing the panels are inevitable tasks for notebook computer manufacturers. e accuracy of the classification would affect the profit of the notebook computer manufacturer and the market competitiveness of the brand
Summary
Defects on thin film transistor-liquid crystal displays (TFTLCD) can be divided into macrodefects and microdefects [1]. On the other hand, using automatic optical inspection (AOI) technology to assist visual inspection may reduce the risk of damage to inspectors’ eyes and can improve the efficiency of the panel sorting process. Ey compared the ability to detect pixel-level defects by using RetinaNet [17], M2Det [18], and Yolov3 [19] network and found that RetinaNet based architecture provides balanced results in terms of accuracy and use of time. Is paper aimed at constructing a multicategory classification model based on deep learning that could be used to improve the efficiency of model construction to identify defective pixels in TFTLCD panels while maintaining high classification accuracy.
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