A TFT-LCD Defect Classification Model Based on Convolutional Neural Network

• Main Authors: Huang, Chun-Chieh, 黃駿杰
• Other Authors: Chang, Yung-Chia
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/25awgz

碩士 === 國立交通大學 === 工業工程與管理系所 === 106 === Since the ever-changing technology nowadays, people are increasing their request for skill and qualities as well as the display screens. As the level of the quality of display screens rise, the numbers of dead dots that used to be the inspection standard become a problem that can’t be ignored. Traditional inspection methods that inspected by humans may cause occupational injuries and fatigue and also harm the yield rate. Therefore, the trend of using Automatic Optical Inspection (AOI) instead of the traditional way is inevitable. This research is based on AOI, constructing a TFT-LCD defect classification model based on Convolutional Neural Network, evidenced with practical data provided by one well-known laptop brand in Taiwan. The related researches for dead dots recently are most thorough machine learning which processes images and calculates feature values first and finally classifies it with the algorithm. The breakthrough of this research is that there is no need to process images and calculate feature values. Besides, the distinguish rate is up to 99.4% so that we can say it’s effective to classify dead dots for TFT-LCD.