72‐1: In‐Line Mura Detection using Convolutional Neural Network in Display Manufacturing. Issue 1 (28th June 2022)

• Record Type: Journal Article
• Authors: Tomita, Satoru; Siritanawan, Prarinya; Kotani, Kazunori
• Is Part Of: Digest of technical papers. Volume 53:Issue 1(2022)
• Page Start: 963
• Page End: 966
• Publication Date: 2022-06-28
• Subjects: mura -- detection -- display -- OLED -- machine learning -- CNN (Convolutional Neural Network) -- DNN (Deep Neural Network) -- IPQC (In Process Quality Control) -- contrast-enhancement -- subspace method; Information display systems -- Congresses; 621.3815422
• DOI: 10.1002/sdtp.15656
• Languages: English
• ISSNs: 0097-966X

Abstract

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.