- 學位論文
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-島狀半導體光罩瑕疵自動分類系統開發
Development of an Automatic Semiconductor Electrode Mask Defect Classification System for the Inline Inspection of TFT-LCD Array Engineering
摘要
近年來面板被大量的使用，使得其需求量大增，因此所有面板廠無不增加面板產量和研發新尺寸。然而在面板的製造流程中，面板常會因外在環境及機台本身之因素出現瑕疵（defect），造成面板良率下降、成本增加，為了改善此問題，目前面板廠皆設有線中檢查機制，即在製造流程中進行玻璃基板抽樣檢查的工作，以防止大量不良面板之發生，但目前的檢查機制僅能進行抽檢動作，且皆為人工處理，為取代人力、增加處理速度，讓面板進行全檢並即時傳送相關資訊給線上工程師，因此發展一套即時且自動化之檢查機台系統是有必要的。 本論文為改善線中檢查機制，提昇面板廠之良率，因此發展了一套自動化之瑕疵偵測及辨識系統，藉由使用影像處理技術、統計方法、紋理特徵值抽取和類神經網路等方法，針對檢查機台所拍攝之瑕疵影像進行分類。為符合面板廠的使用需求，因此利用中華映管面板廠所提供之瑕疵影像進行分析及辨識，在此本論文主要針對檢查機台在陣列電路工程中、第二道光罩微影製程中所拍攝到之瑕疵影像進行分析與辨識，發展了一個『島狀半導體光罩中微影製程瑕疵影像辨識系統』。此系統可以對『閘電極-儲存電容短路』、『SE殘』、『SE殘連橫』、『異物』、『塗佈異常』、『刮傷』、『膜浮起及膜剝落』、『皺膜剝及皺膜浮』等瑕疵影像進行分析及辨識，讓瑕疵分類達到自動化。 經由實驗測試可知，將面板廠所提供之987張瑕疵影像輸入『島狀半導體光罩中微影製程瑕疵影像辨識系統』進行分析及辨識，整體辨識率可達97％以上，且達到高速辨識(4秒) 之目的，即顯示整套系統能有效的針對上述的瑕疵影像進行快速且精準的分類，以防止面板再發生相同瑕疵，並達到工廠降低成本提高良率的要求。
並列摘要
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 SE(Semiconductor 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 987 defect pictures offered by a listed panel company in Taiwan, the “Defect Recognition System for the Lithography Process Inspection in the SE-Mask” achieves a recognition rate higher than 97%. 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 4 second, which means that the goal of high-speed defect inspection is achieved.