應用視覺及資料探勘技術於TFT-LCD陣列電路工程線中瑕疵辨識之研究-源/汲電極光罩瑕疵自動分類系統開發

Abstract

For panel companies today, reducing their costs and increasing company revenues by raising the yield rate of panels is one of their most important goals. One of the major factors hugely affecting the quality of panels is the process of making panels. Though all of panel-making processes are practiced in dust-free rooms, and all factories have tried their best to reduce the influences of outside factors to the least degree, defects do occur in the process, either because of the falling-down of particles, or because of equipment failure. As a result, companies make defected products. For example, some of the defects are bright spots or bright lines that do not change color for a long time. Less serious defects could be repaired by laser; more serious ones make the product being abandoned. This is an unnecessary waste of resources for a company. As a result, if mistakes could be found in time in the process of array engineering and get fixed or prevented, the yield rate of panels would thus be increased, and the cost decreased. Most companies set up quality control departments to increase the yield rate of their products. They are meant to perform manual defect classification when the process is practiced to a certain stage, intending to call a stop before major mistakes occur. However, in order to reduce the influences of human factors, to accelerate the speed of processing, and to achieve the goal of a full inspection, an automatic inspection system is in great need. The purpose of this study is to provide an automatic defect recognition system. In this study, we consult theories of digital image processing techniques, statistic textured feature extraction, data mining, and neural network. We want our system to automatically classify defect images shot by inspection machines, with the intension of increasing the yield rate of products. We focus on the analysis and recognition of defect images shot by inspection machines in array engineering, during the lithography process in the third mask, and devised a “Defect Recognition System for the Lithography Process Inspection in the SD(Source and Drain Electrode)-Mask” in the study. This system is able to automatically classify nine common defect images, providing a real-time automatic defect classification. The above-mentioned defect images are all offered by a panel company. The experimental results show that, among the 886 defect pictures offered by a listed panel company in Taiwan, the “Defect Recognition System for the Lithography Process Inspection in the SD-Mask” achieves a recognition rate higher than 96%. This means that our system is able to classify the nine defect images above promptly and accurately, to prevent the same defects occur in panels-to-come during the lithography process, and is capable of increasing the accuracy of inspection and the yield rate in panel processing. Moreover the developed system is also to classify one defect image within 3 second, which means that the goal of high-speed defect inspection is achieved.