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研究生:陳孟亨
論文名稱:應用類神經網路於TFT-LCD微影製程缺陷辨識之研究
論文名稱(外文):A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process
指導教授:蘇朝墩蘇朝墩引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:32
中文關鍵詞:缺陷TFT-LCD微影製程類神經網路
外文關鍵詞:DefectThin-Film-Transistor (TFT)Liquid Crystal Display (LCD)photolithography processneural-network
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在TFT-LCD 的微影製程中,由於製程本身的精密要求,良率的控制便扮演著舉足輕重的角色。在微影製程結束欲進入下個製程以前,往往利用自動光學檢查 (AOI) 將面板上的缺陷位置記錄下來而形成缺陷影像。這些缺陷影像再由有經驗的工程師或作業員以人工辨識的方式歸類其發生的原因。顯然的,這種以人工辨識的方式可能會因為個人的差異,導致淺在的誤判或時間上的浪費。本篇論文提出一應用類神經網路於TFT-LCD微影製程缺陷辨識之方法,並利用四種神經網路:倒傳遞神經網路,放射基準機能網路,學習向量量化網路1 (LVQ1),學習向量量化網路2 (LVQ2),來探討最後的執行結果與進行比較。研究的結果發現本論文所提出的方法可有效的進行微影製程缺陷之辨識。
Since the advent of high qualification and tiny technology, yield control in the photolithography process has played an important role in the manufacture of Thin Film Transistor-Liquid Crystal Displays (TFT-LCDs). Through an Auto Optic Inspection (AOI), defect points from the panels are collected and the defect images are generated after the photolithography process. The defect images are usually identified by experienced engineers or operators. Evidently, human identification may produce potential misjudgments and cause time loss. This study therefore proposes a neural-network approach for defect recognition in the TFT-LCD photolithography process. There were four neural-network methods adopted for this purpose, namely, backpropagation, radial basis function, learning vector quantization 1, and learning vector quantization 2. A comparison of the performance of these four types of neural-networks was illustrated. The results show that the proposed approach can effectively recognize the defect images in the photolithography process.
Contents
Abstract …………………………………………………………………………… I
摘要 …………………………………………………………………………… II
誌謝 …………………………………………………………………………… III
Contents …………………………………………………………………………… IV
List of Tables …………………………………………………………………………… V
List of Figures …………………………………………………………………………… VI
Chapter 1 Introduction……………………………………………………………… 1
Chapter 2 Defect Recognition Problem in TFT-LCD Photolithography Process…… 3
 2.1 Photolithography Process ……………………………………………… 3
 2.2 Defect Recognition …………………………………………………… 5
Chapter 3 Proposed Approach……………………………………………………… 7
 3.1 Image Data Collection…………………………………………………… 8
 3.2 Image Binarization……………………………………………………… 8
 3.3 Mask Size Decision……………………………………………………… 9
 3.4 Neural-Network Modeling……………………………………………… 12
Chapter 4 Implementation…………………………………………………………… 14
 4.1 Image Data Collection…………………………………………………… 14
 4.2 Image Binarization……………………………………………………… 16
 4.3 Mask Size Decision……………………………………………………… 17
 4.4 Neural-Network Modeling……………………………………………… 19
 4.5 A Comparison…………………………………………………………… 22
Chapter 5 Conclusions……………………………………………………………… 24
References …………………………………………………………………………… 25
Appendix …………………………………………………………………………… 27
References
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