Construction of Multi-Category Classification Model for Identifying Defective Pixels on TFT-LCD Panels Using Deep Learning Approach

• Main Authors: Chiu, Hung-Chih, 邱泓智
• Other Authors: Chang, Yung-Chia
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/4v73kv

碩士 === 國立交通大學 === 工業工程與管理系所 === 107 === Amount and position of defects on TFT-LCD panel affects the level of the panel, adding Automatic optical inspection(AOI) into the light-on test, which can automatically detect defects from the image of LCD panel. This study constructed a model based on Convolutional neural network of deep learning, which can classify various kind of defects at once. Without any pre-processing, the model can automatically capture features through a large number of data and model training, resulting in a high-efficiency and high-accuracy defects classification model. This study used actual panel images from Taiwan's leading computer hardware manufacturers for model construction, model testing and validating the result. After validation, the model constructed by this study has 99.9% model accuracy and excellent specificity and sensitivity, the model can also finish the process of classifying a TFT-LCD panel in only 467 seconds.