現在OLED (Organic Light Emitting Diode)製造業尚未有一套相當準確的OLED表面檢測方式，仍是以人工或是以色度計檢測為主，此種檢測方式耗費人力、容易因疲勞而造成誤判，而且檢測工具相當昂貴，所以本論文針對OLED表面瑕疵檢測，應用影像處理方法來開發一套自動瑕疵檢測系統。本論文中規劃檢測的OLED表面瑕疵可分為三種：刮痕、亮點和擊穿。在影像處理方法，我們先利用中值濾波器減少脈衝雜訊，再使用一次高通濾波器，將影像特徵突顯出來。由於瑕疵影像與背景灰階差異不大，以固定門檻值分割瑕疵影像會造成無法分割所有圖形的瑕疵類別，因此使用統計式門檻值決定法，以選擇最佳門檻值來分割出瑕疵區域，再配合形態學中的開放運算使瑕疵輪廓更平滑完整並侵蝕小雜訊。最後選擇緊緻性、熵(Entropy)及外接圓瑕疵面積比重做為準備擷取的瑕疵特徵。
本論文中收集81個瑕疵樣本，以支援向量機(Support Vector Machine)做為分類器。首先，我們以亂數方式選取15筆、20筆、25筆、30筆及40筆樣品，每個樣品同樣選出3組樣品集，再以各組對全體樣品執行測試，來比較各組的測試率，以選出最佳的測試組合。結果顯示訓練樣本為15筆時，其測試率皆可達到95%以上；訓練樣本為20筆時，其測試率皆可達到96%以上；而在訓練樣本為25筆以上時，其測試率皆可達到100%，此實驗驗證了瑕疵特徵選取的適當，以及支援向量機分類的可靠性，因此本檢測系統可成功被應用於OLED表面瑕疵之檢測。

At present, the OLED (Organic Light Emitting Diode) manufacturing industry has not a way of quite accurate OLED surface detection yet. The way of inspect is still by the man-power or by the chromometer primarily. This kind of detection consumes lots of man-power, and makes mistakes easily. Moreover, the tool of detection is quite expensive. In this thesis, an automatic inspection system for the OLED surface defects was developed based on image processing. The OLED surface defects can be divided into three kinds: scratch, luminescent spot and puncture. In image processing, the median filter is first used to reduce the impulse noise of images. Secondly, the high pass filter is used to sharpening the edge pixel. Because the gray values of all defects are close to those of the background, it is difficult to separate the image by a fixed threshold value. First of all, the statistical threshold value decision method is used to choose optimal threshold values with the difference of gray values in image segmentation. Secondly, the opening operator in morphology is used to smooth the contour of defects and erode slight noise. Finally, the compactness, entropy and the specific gravity of the circumscribed circle and defection area are selected as defect features.
In this thesis, eighty one defect samples are collected, by using the SVM (Support Vector Machine) to do the classification. First, the fifteen, twenty, twenty five, thirty, and forty samples are selected randomly. Secondly, the same steps are repeated to take out other two sets just like each of the defect sample set. Finally those sets are use to test the entire samples and we record the recognition rate. When the fifteen samples are used as training data, we can get the recognition rate above 95%. When the twenty are used as training data, we can get the recognition rate above 96%; But when the training samples more than twenty five, we can get the recognition rate of 100%. This experiment proves suitability of the defect features and the reliability of SVM classifier. So, this inspection system can be applied in the OLED surface defect successfully.

[1] Y. S. Na, O. K. Kwon, “A Signal Chip Driver System for 1.2-inch Organic Electro-Luminescent Display,” IEEE Trans. on. Consumer Electronics, Vol. 48, No. 2, pp. 220-228, 2002.
[2] C. J. Juan, M. J. Tsai and S. W. Hong, “ Fast Measurement Inspection Method for Inspecting OLED Panels,” EDMS Symposium, Keelung, 2003.
[3] P. Pope, H. P. Kallmann and P. J. Magnante, Journal of Chemical Physics, Vol. 38, pp, 2042, 1963.
[4] C. W. Tang and S. A. Van Slyke, “Organic Electroluminescent Diodes,” Applied Physics Letters, Vol. 51, pp. 913-915, 1987.
[5] M. Wohlgenannt, K. Tandon, S. Mazumdar, S. Ramasesha and Z. V. Vardeny, Nature, pp, 409, 494, 2001.
[6] M. A. Baldo, D. F. O’Brien, Y. You, A. Shoustikov, S. Sibley and M. E. Thompson, S. R. FoR. Forrest, Nature(London), pp, 395, 151, 1998.
[7] A. Sempel and M. Buchel, Organic Electronics, pp, 3, 89, 2002.
[8] 陳光榮、林彥仲，光訊雜誌第98期，pp. 23，2002。
[9] 顧鴻壽，光電平面面板顯示器基本概論，2004。
[10] Stanford Resource Display Insight, “Organic Light-Emitting Diode Displays”, Emerging Display Technologies, Annual Report, 2003.
[11] 城戶淳二，圖解有機EL，世茂出版社，2004。
[12] 陳志忠，液晶顯示器的像素點缺陷與相對亮度均一性之自動化檢 測，私立中原大學機械工程學系碩士論文，2001。
[13] 陳志強，影像處理技術應用於PLED噴墨基板瑕疵檢測，國立中央大學資訊工程研究所碩士論文，2003。
[14] 李孟坤，高分子有機發光二極體瑕疵自動光學檢測系統，國立交通大學工業工程及管理學系碩士論文，2004。
[15] 江嘉明，高分子聚合物發光二極體之電腦視覺輔助瑕疵檢測系統，國立台灣大學資訊工程研究所碩士論文，2003。
[16] 周千惠，彩色OLED亮度檢測及補償機制之研究，國立台灣科技大學自動化及控制研究所碩士論文，2006。
[17] 羅新弦，主動式有機電激發光二極體顯示器畫素驅動電路之分析與設計，私立長庚大學光電工程研究所碩士論文，2004。
[18] H. F. Ng, “Automatic Thresholding for Defect Detection,” Pattern Recognition Letters, Vol. 27, No. 14-15, pp. 1644-1649, 2006.
[19] J. N. Kapur, P. K. Sahoo and A. K. C. Wong, “A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram,” Computer Vision, Graphics and Image Processing, Vol. 29, No. 3, pp. 273-285, 1985.
[20] S. J. Ko and Y. H. Lee, “Center Weighted Median Filters and Their Applications to Image Enhancement,” IEEE Trans. on Circuits and Systems, Vol. 38, No.9 , pp. 984-993, 1991.
[21] 徐國政，皮革瑕疵類別自動辨識與補償，逢甲大學工業工程學系碩士論文，2002。
[22] B. C. Jiang, S. L. Tasi and C. C. Wang, “Machine Vision-Based Gray Relational Theory Applied to IC Marking Inspection,” IEEE Trans. on Semicond. Manuf., Vol. 15, No. 4, pp. 531-539, 2002.
[23] 陳志強，有機發光二極體顯示器技術，全華圖書股份有限公司，2007。
[24] 鐘國亮，影像處理與電腦視覺第三版，東華書局，2006。
[25] N. Otsu, “A Threshold Selection Method from Gray Level Histogram,” IEEE Trans. on System, Man and Cybernetic, pp. 62-66, 1978.
[26] R. Jain, R. Kasturi and B. G. Schunck, “Machine Vision”, McGraw-Hill, 1995.
[27] 陳建堂，偏光膜外觀瑕疵之影像檢測系統開發，台灣科技大學高分子工程學系碩士論文，2008。
[28] B. E. Boser, I. Guyon, and V. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152, 1992.
[29] C. J. C. Burges “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998.
[30] B. Schlkopf, C. J. C. Burges and A. J. Smola, “Introduction to Support Vector Learning, Advances in Kernel Methods-Support Vector Learning,” Cambridge, 1999.
[31] 蘇文彬，運用多核心支援向量機於偽鈔辨識，中山大學電機工程學系碩士論文，2008。
[32] 粘志鵬，基於支援向量機之中文自動作文評分系統，交通大學資訊科學與工程學系碩士論文，2006。