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研究生: 方咨螢
Tz-ying Fang
論文名稱: 應用適應性傅立葉分析法於自動化發光二極體封裝元件外觀瑕疵檢測系統之開發
Application of Adaptive Fourier Analysis to Development of Automatic Detection System for Light Emitting Diode Package Component Appearance Defect
指導教授: 郭中豐
Chung-Feng Kuo
口試委員: 黃昌群
Chang-Chiun Huang
邱錦勳
Chin-hsun Chiu
張維哲
none
學位類別: 碩士
Master
系所名稱: 工程學院 - 材料科學與工程系
Department of Materials Science and Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 中文
論文頁數: 95
中文關鍵詞: LED封裝檢測局部影像強化熵值資訊紋理檢測適應性傅立葉分析
外文關鍵詞: LED package inspection, local image enhancement, entropy information, texture detection, adaptive Fourier analysis
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本研究係開發表面黏著型封裝發光二極體(surface-mount device light emitting diode, SMD-LED)瑕疵檢測系統,針對LED封裝元件常見且重要的瑕疵進行檢測,包含缺件、無固晶、銲線偏移、銲線斷線及異物瑕疵,進行檢測分類以提升產品良率與生產速度。
本研究首先利用直方圖灰階特性作為缺件瑕疵的快速篩檢指標,接著運用快速相關係數法進行元件及銲點定位,並以最大相關係數值作為無固晶瑕疵判斷指標,而為克服銲線容易受光線影響造成不易判讀的現象而導致強化效果不佳,本研究提出改良麥克森相似對比(Michelson-like contrast, MLC)影像強化,使其能容忍更大的背景灰階變化成功將銲線強化,並以直方圖熵值資訊自動選取最佳分割門檻值,最後以紋理異常檢測的概念提出多尺度適應性傅立葉分析法(multiscale adaptive Fourier analysis)達到異物瑕疵檢測,以降低元件間尺寸及內部電極的些微差異與銲線的位置及形態不固定對檢測結果之影響。本研究最後以實際樣本進行實務驗證,結果顯示本研究提出的方法與相位轉換法(phase-only transform, PHOT)及多尺度相位轉換分析法(multiscale phase-only transform, MPHOT)相比之下較能保留瑕疵的形狀及面積特徵。
最後本研究所提出的檢測系統,在缺件瑕疵的快速篩檢及無固晶瑕疵的檢測平均時間分別為0.012秒及0.63秒,辨識率均為100%;銲線檢測平均需0.42秒,辨識率為98.17%;而異物檢測平均時間為0.65秒,辨識率為98.53%,系統整體辨識率為98.25%,總處理時間平均需1.54秒,由實驗結果可知本研究所提出之檢測系統有助於提升LED產業競爭力。


This research develops a surface-mount device light emitting diode defect detection system for detecting common and important defects in LED package component, including missing component, no chip, wire shift, wire broken and foreign material, the detections are classified to increase the product yield and production rate.
The gray scale characteristic of histogram is used as the rapid sieving analysis indicator of missing component defect, and then the component and solder joint are positioned by using fast normalized cross-correlation, and the maximum correlation coefficient value is used as judgment indicator of no chip defect. In order to overcome the difficult identification of wire solder in the light that may result in poor enhancement, this research proposes improving Michelson-like contrast (MLC) image enhancement, so as to bear larger background gray scale change to enhance the wire solder, and the optimum segmentation threshold is selected automatically by the entropy of histogram. Finally, the multiscale adaptive Fourier analysis is proposed in the concept of texture anomaly detection for foreign material defect detection, so as to reduce the effects of component dimensions and slight difference in internal electrode and unfixed position and form of wire solder on the detection result. Finally, the actual sample is used for practical validation. The result shows the method proposed in this research and phase-only transform and multiscale phase-only transform can maintain the shape and areal features of defects successfully.
Finally, the mean time spent by the detection system proposed in this research on rapid sieving analysis of missing component defect and on the detection of no chip defect is 0.012 second and 0.63 second respectively, the recognition rate is 100%. The wire solder detection takes 0.42 second on average, the recognition rate is 98.17%; the mean time to detect foreign material is 0.65 second, the recognition rate is 98.53%, the overall recognition rate of system is 98.25%, the average total processing time is 1.54 seconds. The experimental result shows that the detection system proposed in this research actually contributes to enhancing the competitiveness of LED industry.

摘要 I Abstract II 致謝 IV 目錄 V 圖目錄 VIII 表目錄 XI 第1章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 2 1.2.1 LED封裝元件瑕疵檢測及銲線檢測 3 1.2.2 數位影像處理技術 5 1.2.3 非監督式紋理異常檢測 9 1.3 研究規劃及目的 11 1.4 論文架構 12 第2章 LED封裝元件介紹 15 2.1 SMD-LED封裝製程 15 2.2 SMD-LED元件結構及發光原理 16 2.3 瑕疵種類及成因介紹 17 第3章 研究方法與理論 21 3.1 灰階直方圖 21 3.2 快速正規化相關係數法 22 3.3 座標相對位移法 26 3.4 雜訊濾除 27 3.4.1 離散傅立葉轉換 27 3.4.2 高斯低通濾波器 28 3.4.3 三維區塊匹配雜訊濾除 29 3.5 影像強化 33 3.5.1 麥克森相似對比影像強化 33 3.5.2 改良麥克森相似對比影像強化 35 3.6 直方圖熵值局部最小值 36 3.7 形態學 39 3.7.1 侵蝕 39 3.7.2 膨脹 40 3.7.3 斷開運算與閉合運算 41 3.7.4 連通物件標記 41 3.8 迴歸分析 43 3.8.1 多項式擬合 43 3.8.2 擬合度 45 第4章 多尺度適應性傅立葉分析瑕疵檢測 46 4.1 高斯金字塔 46 4.2 適應性傅立葉遮罩 49 4.3 馬氏距離 52 4.4 非線性權重補償與影像融合 53 4.5 影像梯度 54 第5章 實驗結果及驗證 56 5.1 影像擷取系統及程式開發 57 5.2 實驗結果 60 5.2.1 缺件瑕疵的快速篩檢 60 5.2.2 無固晶瑕疵的辨識 64 5.2.3 銲線檢測流程及結果 68 5.2.4 異物檢測流程及結果 76 5.2.5 系統整體辨識率 86 第6章 結論 88 參考文獻 90

[1] 張祐銜、劉正毓,發光二體的封裝技術,科學發展,435期,2009。
[2] Photonics Industry and Technology Development Association, “Taiwan Optical Communication Industry grows nearly 10% in 2011”, PIDA, December, 2011.
[3] 南韓:韓聯社,2014年各國禁用白熾燈韓LED市場將獲益,國際在線,網址:http://big5.chinabroadcast.cn/gate/big5/gb.cri.cn/44571/2013/10/23/6112s4294836.htm,2013,上網日期:2014/04/28。
[4] 電子工程專輯,LED inside預估2013年全球LED產值124億美元,網址:
http://www.eettaiwan.com/ART_8800680258_480702_NT_aa10d93c.HTM#,2013,上網日期:2014/06/26。
[5] D. B. Perng, H. W. Liu, and C. C. Chang, “Automated SMD-LED Inspection Using Machine Vision”, International Journal of Advanced Manufacturing Technology, Vol. 57, No. 9-12, pp. 1065- 1077, 2012.
[6] 張凱翔,“表面黏著型發光二極體光學檢測系統開發”,國立中山大學,機械與機電工程學系,碩士論文,2012。
[7] H. Liu, W. Zhou, Q. Kuang, L. Cao, and B. Gao, “Defect Detection of IC Wafer Based on Two-dimension Wavelet Transform”, Microelectronics Journal, Vol. 41, No. 2-3, pp. 171-177, 2010.
[8] H. Liu, W. Zhou, Q. Kuang, L. Cao, and B. Gao, “Defect Detection of IC Wafer Based on Spectral Subtraction”, IEEE Transactions on Semiconductor Manufacturing, Vol. 23, No. 1, pp. 141-147, 2010.
[9] C. C. Wang, B. C. Jiang, J. Y. Lin, and C. C. Chu, “Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient”, IEEE Transactions on Semiconductor Manufacturing, Vol. 26, No. 3, pp. 378-384, 2013.
[10] Q. Z. Ye, S. H. Ong, and X. Han, “A Stereo Vision System for the Inspection of IC Bonding Wires”, International Journal of Imaging Systems and Technology, Vol. 11, No. 4, pp. 254-262, 2000.
[11] D. B. Perng, C. C. Chou, and S. M. Lee, “Illumination System for Wire Bonding Defect Inspection”, Applied Optics, Vol. 46, No. 6, pp. 845-854, 2007.
[12] D. B. Perng, C. C. Chou, and S. M. Lee, “Design and Development of a New Machine Vision Wire Bonding Inspection System”, International Journal of Advanced Manufacturing Technology, Vol. 34, No. 3-4, pp. 323-334, 2007.
[13] M. A. Sutton, M. Cheng, W. H. Peters, Y. J. Chao, and S. R. Mchneill, “Application of an Optimized Digital Correlation Method to Planar Deformation Analysis”, Image and Vision Computing, Vol. 4, No. 3, pp. 143-150, 1986.
[14] J. P. Lewis, “Fast Normalized Cross Correlation”, Vision Interface, pp. 120-123, 1995.
[15] B. Kai and D. H. Uwe, “Templete Matching Using Fast Normalized Cross Correlation”, Journal of Electronic Imaging, Vol. 4387, No. 1, pp. 95-102, 2001.
[16] P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629-639, 1990.
[17] S. M. Chao and D. M. Tsai, “An Improved Anisotropic Diffusion Model for Detail-smoothing and Edge-preserving”, Pattern Recognition Letters, Vol. 31, No.13, pp. 2012-2023, 2010.
[18] A. Buades, B. Coll, and J. M. Morel, “A Non-local Algorithm for Image Denoising”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 60-65, June, 2005.
[19] P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Bayesian Non-local Means-based Speckle Filtering”, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, pp. 1291-1294, May, 2008.
[20] P. Coupe, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal Means-based Speckle Filtering for Ultrasound Images”, IEEE Transactions on Image Processing, Vol. 18, No. 10, pp. 2221-2229, 2009.
[21] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising with Block-matching and 3D Filtering”, Journal of Electronic Imaging, Vol. 6064, No. 6064A-30, pp. 1-12, 2006.
[22] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3D Transform-Domain Collaborative Filtering”, IEEE Transactions on Image Processing, Vol. 16, No. 8, pp. 2080- 2095, 2007.
[23] 繆紹剛,數位影像處理3/e,培生教育出版集團,2005。
[24] Y. C. Chang, C. M. Chang, “A Simple Histogram Modification Scheme for Contrast Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, pp. 737-742, 2010.
[25] Z. Chao, C. Qian, X. Sui, “Range Limited Bi-Histogram Equalization for Image Contrast Enhancement”, Optik-International Journal for Light and Electron Optics, Vol. 124, No. 5, pp. 425-431, 2013.
[26] T. Celik and T. Tjahjadi, “Contextual and Variational Contrast Enhancement”, IEEE Transactions on Image Processing, Vol. 20, No. 12, pp. 3431-3441, 2011.
[27] T. Celik, “Two-Dimensional Histogram Equalization and Contrast Enhancement”, Pattern Recognition, Vol. 45, No. 10, pp. 3810-3824, 2012.
[28] Q. Li and S. Ren, “A Visual Detection System for Rail Surface Defects”, IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Vol. 42, No. 6, pp. 1531-1542, 2012.
[29] N. Otsu, “A Threshold Selection Method from Gray Level Histogram”, IEEE Transactions on Systems Man and Cybernetic, Vol. 9, No. 1, pp. 62-66, 1979.
[30] 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”, Graphical Models and Image Processing, Vol. 29, pp. 273-285, 1985.
[31] T. W. Ridler and S. Calvard, “Picture Thresholding Using an Iterative Selection Method”, IEEE Transactions on Systems Man and Cybernetic, Vol. 8, pp. 630-632, 1978.
[32] O. J. Tobias and R. Seara, “Image Segmentation by Histogram Thresholding Using Fuzzy Sets”, IEEE Transactions on Image Processing, Vol. 11, No. 12, pp. 1457-1465, 2002.
[33] X. Xie, “A Review of Recent Advances in Surface Defect Detection Using Texture Analysis Techniques”, Electronic Letters on Computer Vision and Image Analysis, Vol. 7, No. 3, pp. 1-22, 2008.
[34] D. M. Tsai, and C. Y. Hsieh, “Automated Surface Inspection for Directional Textures”, Image and Vision Computing, Vol. 18, No. 1, pp. 49-62, 1999.
[35] C. Chan and G. Pang, “Fabric Defect Detection by Fourier Analysis”, IEEE Transactions on Industry Applications, Vol. 36, No. 5, pp. 1267-1276, 2000.
[36] D. M. Tsai and T. Y. Huang, “Automated Surface Inspection for Statistical Textures”, Image and Vision Computing, Vol. 21, No. 4, pp. 307-323, 2003.
[37] H. D. Lin and S. W. Chiu, “Flaw Detection of Domed Surfaces in LED Packages by Machine Vision System”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15208-15216, 2011.
[38] D. Aiger and H. Talbot, “The Phase Only Transform for Unsupervised Surface Defect Detection”, IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp. 295 -302, June, 2010.
[39] F. Burger and J. Pauli, “Unsupervised Segmentation of Anomalies in Sequential Data, Images and Volumetric Data Using Multiscale Fourier Phase-Only Analysis”, Scandinavian Conference on Image Analysis, Espoo, Finland, Vol. 7944, pp. 44-53, June, 2013.
[40] 郭浩中、賴方儀、郭守義,LED原理與應用,五南圖書出版股份有限公司,2009。
[41] 沈诘,LED封裝技術與應用,北京,化學工業出版社,2012。
[42] 田民波、呂輝宗、溫坤禮,白光LED照明技術,五南圖書出版股份有限公司,2011。
[43] P. J. Burt, “Fast Filter Transforms for Image Processing”, Computer Graphics and Image Processing, Vol. 16, No. 1, pp. 20-51, 1981.
[44] P. J. Burt and E. H. Adelson, “The Laplacian Pyramid as a Compact Image Code”, IEEE Transactions on Communications, Vol. 31, No. 4, pp. 532-540, 1983.
[45] 新亞洲儀器技術團隊,機器視覺演算法與應用,新亞洲儀器股份有限公司,台中市,2011。
[46] R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, “The Mahalanobis Distance”, Chemometrics and Intelligent Laboratory Systems, Vol. 50, No. 1, pp. 1-18, 2000.
[47] http://www2.cs.uregina.ca/~hamilton/courses/831/notes/confusion_matrix/confusion_matrix.html

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