Dr. Seong Baek Yoon Profile

Dr. Seong Baek Yoon

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Proceedings Article | 20 June 2021 Poster + Presentation + Paper

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, 117870M (2021) https://doi.org/10.1117/12.2592518

KEYWORDS: Data modeling, Inspection, Defect detection, Transistors, Performance modeling, LCDs, Image segmentation, Convolution

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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.

Proceedings Article | 20 June 2021 Poster + Presentation + Paper

Effective deep learning training method using blur and noise filter to detect defect in TFT-LCD PAD

Jae Gu Lee, Yeo Min Yoon, Seon Geol Kim, Chang Woo Ha, Seong Baek Yoon, Hyo Jin Lee

Proceedings Volume 11787, 117870I (2021) https://doi.org/10.1117/12.2592346

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In this paper, we propose a preprocessing method of exploiting noise and blur for effective noise elimination in data. At present, there are many kinds of research to improve the performance of object classification, detection, and image segmentation based on deep learning. For instance, adding noise to data, multiple in-depth convolution layers, and data augmentation have been studied in many ways. An in-depth convolution network results in long processing time and data augmentation gives a burden to memory usage. However, adding noise and blur data preprocessing method gives less burden to hardware, which helps improve algorithm performance. The proposed method is applied to TFT-LCD (Thinfilm Transistor Liquid Crystal Display) PAD defect detection for improved performance. To verify the accuracy and repeatability, 691 actual defect images are used in experiments. These images are composed of complex patterns and defects in the images having barely 2 pixels with little intensity difference. To confirm which filters are better, Gaussian blur, Salt & Pepper noise, and Gaussian noise filters are used for comparison. According to the result, the experiments with Salt & Pepper and Gaussian noise detect all defects. However, the repeatability of the Gaussian noise filter seems better than that with Salt & pepper. Furthermore, applying noise and blur to train data shows more than twice higher detection accuracy than those without such applications. We verified that using Gaussian noise and blur indicates excellent accuracy and repeatability when inspecting the TFT-LCD PAD area in AOI machines.

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