Deep learning model and train method to increase the speed and accuracy in inline TFT-PAD area inspection

Da hyun Park; Seong baek Yoon; Hyun woo Kim; Hyeong jin Kim; Yun hyeok Kim; Hyo jin Lee

doi:10.1117/12.2592518

20 June 2021 Deep learning model and train method to increase the speed and accuracy in inline TFT-PAD area inspection

Da hyun Park, Seong baek Yoon, Hyun woo Kim, Hyeong jin Kim, Yun hyeok Kim, Hyo jin Lee

Proceedings Volume 11787, Automated Visual Inspection and Machine Vision IV; 117870M (2021) https://doi.org/10.1117/12.2592518
Event: SPIE Optical Metrology, 2021, Online Only

Conference Poster

Abstract

In actual industrial sites, the ability of the deep learning model to detect defects at a high speed and reducing the time required to train the model is also a very important issue. In this paper, we propose a fast and accurate deep learning model and training method that can be applied to inspect the TFT-LCD(The Film Transistor - Liquid Crystal Display) PAD area image. The deep learning model we propose is a lightweight model based on U-net. By training only about 250,000 parameters, it was possible to confirm excellent performance in defect segmentation. In addition, a study on train data was also conducted so that the model can learn more effectively. We studied a method of training both normal images (images without defects) and abnormal images (images with defects), and it was confirmed that this performance showed better performance than when only data with defects were learned. It was shown that the method of learning both normal and abnormal results in a 50% or more reduction in the incidence of false judgment images than the method of learning only simple abnormal data.

Conference Presentation

Citation Download Citation

Da hyun Park, Seong baek Yoon, Hyun woo Kim, Hyeong jin Kim, Yun hyeok Kim, and Hyo jin Lee "Deep learning model and train method to increase the speed and accuracy in inline TFT-PAD area inspection", Proc. SPIE 11787, Automated Visual Inspection and Machine Vision IV, 117870M (20 June 2021); https://doi.org/10.1117/12.2592518

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