72‐1: In‐Line Mura Detection using Convolutional Neural Network in Display Manufacturing

Abstract

This paper discusses the automatic detection of mura, which has been a long‐standing challenge in the display industries. Using a dataset of 8000 images of OLED (Organic Light Emitting Diode) displays including four different types of mura, we found that a CNN (Convolutional Neural Network) having four or five sets of convolution and max‐pooling layers can detect mura with the accuracy more than 0.8. To improve detection of low contrast mura, we employed a contrast‐enhancement method and a subspace‐method, and the CNN accuracy improved to 0.868, close to the human visible test. Furthermore, the implementation of an automatic in‐line mura‐detection system using the proposed model is also discussed.

Detection method of organic light-emitting diodes based on small sample deep learning

“…In a recent study [ 52 ], authors utilized Convolutional Neural Networks (CNNs) for the detection of mura defects. The proposed research was evaluated on OLED images and identified four types of mura defects.…”

6‐3: Identifying the Detail Reason of Pixel Defect via Machine Learning Method

“…1a). Pixel defects and line defects with specific shapes are already automatically detected through image processing, and M ura defects, which require complex analysis and algorithms due to uncertain edge shapes, are being detected through improved technology [1][2][3][4][5]. In particular, machine learning and deep learning are being used as core keys for such research.…”