Pretest Gap mura on TFT LCDs using the optical interference pattern sensing method and neural network classification

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/108038

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108038

Title:	Pretest Gap mura on TFT LCDs using the optical interference pattern sensing method and neural network classification
Authors:	蔡章仁;Li, Tung-Yen;Tsai, Jang-Zern;Chang, Rong-Seng;Ho, Li-Wei;Yang, Ching-Fu
Contributors:	資訊電機學院電機工程學系
Keywords:	Classification;Crystal defects;Defects;Image process;Image processing;Inspection;Inspections;Interference;Interference fringes;mura;neural network;Neural networks;Quality control;Sealing materials;Semiconductor devices;Studies;Thin film transistors;thin-film transistor (TFT) liquid crystal (LC) display (LCD) (TFT LCD)
Date:	2013-01-01
Issue Date:	2026-04-23 14:33:06 (UTC+8)
Publisher:	IEEE Industrial Electronics Society;New York: IEEE
Abstract:	摘要： Recently, thin-film transistor liquid crystal displays (TFT LCDs) have had a high demand in the market, which entails careful product quality control and more stringent defect detection procedures. A good defect detection rate is the basic requirement of the quality control process. The use of conventional human visual inspection methods to find the defects in TFT LCDs is simply not accurate enough and consumes a large amount of resources. An automatic defect inspection method is thus necessary for this industry; to find the defects, the type of defects needs to be recognized as well. Here, we propose an inspection procedure based on the optical interference pattern sensing method to find the interference fringes and then use the image processing to enhance the contrast of the interference fringes, thereby increasing the recognition rate for the latter process. The neural network method is used to learn about and identify the defects and their types. This paper focuses on the mura defect inspection and classification method. Before the learning process has begun, the mean squared error was roughly three, but after neural network retraining of these samples, the results showed that the mean squared error was less than 0.01. The defective panels can be sorted out using this method so that the next processing and waste of materials can be avoided. 其他題名： TIE 出版者： New York: IEEE 出版日期： 2013-09-01 出處： IEEE transactions on industrial electronics (1982), 2013-09, Vol.60 (9), p.3976-3982 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2013 識別號： ISSN: 0278-0046 識別號： EISSN: 1557-9948 識別號： DOI: 10.1109/TIE.2012.2207658 識別號： CODEN: ITIED6
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	18	View/Open

社群 sharing

Loading...