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Student Number 953203071
Author Bao-shin Kuo(郭保鑫)
Author's Email Address No Public.
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Department Mechanical Engineering
Year 2007
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title TFT-LCD Mura Defects Automatic Inspection system using Singular Value Decomposition and Optimal division method
Date of Defense 2008-07-09
Page Count 50
Keyword
  • defect detect
  • machine vision
  • Mura
  • TFT-LCD
  • Abstract With the development of technology, LCD(liquid crystal display) has become the core of displays nowadays. The quality of LCD is one of the targets making customers to purchase. To make sure the quality of LCD, defect detection plays an important role in this field.
       Nowadays, most defect detections made by human eyes often causes failure in defect detections and inconsistency of quality. Among these defects, MURA defection is most hardly to be detected. MURA defection  result from the um-uniform brightness which makes traces on LCD. Due to the unapparent defection, it often takes lots of time to detect but often out of judgement. To solve these defections above, this research designs a set of detection system to replace human eyes. 
       On the image of MURA detection, it’s hard to use normal threshold to detect MURA due to the brightness um-uniform of background. For this reason, this research intends to estimate background of LCD. With this method, we hope to eliminate the effect caused by the background to reveal MURA detection. Singular value decomposition (SVD) expands original image into several based images-these based images are composed of eigenvalue and responded eigenvector. Therefore, the bigger eigenvalue is, the more important feature the original image is. The um-uniform of background is major component, so we take the biggest eigenvalue and responded eigenvector representing major element — the background. Actually, from this experiment we know that the background of the samples is more complex, so we must to separate several blocks to achieve detection of goal. For above reasons, we provide an optimum method to overcome the problem and add the value of SEMU (which is an ergonomics experiment completed by Semiconductor Equipment Materials International, SEMI) to identify real Mura. 
       In software, we complete an interface of automatic system of detection Mura which can provide related information of defect.
    Table of Content 中文摘要             I
    ABSTRACT             II
    致 謝             IV
    目 錄             V
    圖目錄            VIII
    表目錄             X
    第一章 緒論         1
      1.1  前言         1
      1.2  研究背景與動機    2
      1.3  文獻回顧         2
       1.3.1 國內相關研究文獻回顧  2
       1.3.2 國外相關研究文獻回顧  3
      1.4  論文架構         4
    第二章 Mura的定義與特性分析    5
      2.1  Mura的定義    5
      2.2  Mura瑕疵的種類    6
      2.3  Mura缺陷檢測之問題    11
       2.3.1 人眼辨識的問題    11
       2.3.2 背景不均的問題    11
    第三章 研究方法        13
      3.1  問題探討        13
      3.2 方法介紹        14
       3.2.1 硬體校正    14
       3.2.2 背景估測    16
       3.2.3 閥值分離    17
       3.2.4 中值濾波    17
       3.2.5 型態學        19
       3.2.6 標籤化        21
    第四章 硬體架構        23
      4.1  檢測系統架構    23
       4.1.1 影像擷取硬體    23
       4.1.2 點燈模組    23
       4.1.3 人機介面軟體    24
    第五章 實驗成果        26
      5.1  Mura檢測方法與實作    26
      5.2  分區        28
       5.2.1 分區處理    28
       5.2.2 分區數量    28
       5.2.3 參數的強健性    31
      5.3  Mura辨識(SEMU指標值)   33
      5.4  實驗結果        36
    第六章 結論與未來展望    47
    參考文獻             48
    Reference [1] 陳志忠,“液晶顯示器的像素點缺陷與亮度均一性之自動化檢測”,私立中原大學機械工程研究所,碩士論文,2001。
    [2] 錢志豪,“建構液晶顯示器(LCD)色彩偏差瑕疵之自動化視覺檢測系統之探討”,私立朝陽科技大學工業管理研究所,碩士論文,2003。
    [3] 劉祥吉,“液晶顯示器影像顯示不均檢測之研究”,私立元智大學工業工程與管理研究所,碩士論文,2003。
    [4] 楊順霖,“基於奇異值分解法運用於薄膜電晶體液晶顯示器Mura瑕疵之自動化檢測技術”,國立台北科技大學自動化科技研究所,碩士論文,2005。
    [5] J. Y. Lee and S. I. Yoo, “Automatic Detection of Region-Mura Defect in TFT-LCD”, IEICE TRANS, 2004.
    [6] Y. C. Song, D. H. Choi and K. H. Park, “Multiscale Detection of Defect in Thin Film Transistor Liquid Crystal Display Panel”, Japanese Journal of Applied Physics, Vol. 43, No. 8A, pp. 545-548,2004.
    [7] B. C. Jiang, C. C. Wang and H. C. Liu, “Liquid Crystal Display Surface Uniformity Defect Inspection Using Analysis of Variable Exponentially Weighted Moving Average Techniques ”, International Journal of Production Research, vol.43, pp. 67-80, 2005.
    [8] VESA Flat Panel Display Measurement Standard Ver.2.0, June 1, 2001.
    [9] Definition of measurement index (semu) for luminance MURA in FPD image quality inspection, SEMI standard : SEMI D31-1102, 2002.
    [10] J. J. Chang, C. Yao, H.C. Cheng,“Investigation of Defect Inspection Technology on TFT-LCD Manufacturing”, 科技新儀,pp. 23-25,民國九十二年十月.
    [11] 藍玉屏,徐祥瀚,鍾宗穎,“液晶顯示器MURA缺陷量化標準之研究”,第七屆2007全國AOI論壇與展覽,pp. 40-50,國立交通大學,新竹市。2007年10月。
    [12] J. H. Oh, W. S. Kim, “Defect Detection of TFT-LCD Image Using Adapted Contrast Sensitivity Function and Wavelet Transform”, IEICE TRANS ELECTRON, vol. E90-C, 2007
    [13] 賴劍煌、馮國灿,數字圖像處理疑難解析,機械工業出版社,中華人民共和國,2005年11月。
    [14] Alasdair McAndrew著,數位影像處理,徐曉珮譯,高立圖書有限公司,2007年1月。
    [15] 余明興等編著,Borland C++ Builder 6 程式設計經典,文魁資訊股份有限公司,台灣,2007年10月
    [16] 黃文吉,C++ Builder 與影像處理,儒林圖書有限公司,台灣,2007年3月。
    [17] 井上誠嘉等編著,C語言數位影像處理,吳上立、林宏墩譯,全華科技圖書股份有限公司,台灣,2007年1月。
    [18] 涂志偉,“薄膜電晶體液晶顯示器Mura之分析”,國立中央大學電機工程研究所,碩士論文,2005年。
    [19] 鍾國亮,影像處理與電腦視覺,東華書局股有限公司,台灣,2002年。
    [20] 張瑞顯,“應用線性迴歸診斷法於液晶顯示器Mura缺陷自動化檢測之設計與實現”,國立成功大學製造工程研究所,碩士論文,2004年。
    Advisor
  • Pi-Cheng Tung(董必正)
  • Files
  • 953203071.pdf
  • disapprove authorization
    Date of Submission 2008-07-16

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