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研究生:東峻平
研究生(外文):Chun-ping Tung
論文名稱:應用影像處理技術及最佳化多類支持向量機於發光二極體晶粒表面色差與微瑕疵檢測系統之開發與研究
論文名稱(外文):The Development and Research on Using Image Processing Technology and Optimization Multi-Class Support Vector Machines in the Light-Emitting Diodes Chip Surface Color Aberration and Micro-Defects Inspection System
指導教授:郭中豐郭中豐引用關係
指導教授(外文):Chung-Feng Kuo
口試委員:郭中豐
口試日期:2012-07-26
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:自動化及控制研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:147
中文關鍵詞:決策樹支持向量機Otsu門檻值分割法形狀相關係數模板匹配高斯平滑門檻值分割法多類支持向量機分類模組
外文關鍵詞:Multi-Class Support Vector Machine ModelDecision Tree Support Vector MachineOtsu Threshold Segmentation MethodGaussian Smoothing Minimal ThresholdShape-Base Template Matching
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本研究『應用影像處理技術及最佳化多類支持向量機於發光二極體晶粒表面色差與微瑕疵檢測系統之開發與研究』主要針對晶粒(Chip)製程的檢測階段進行自動化系統之最佳化設計,包含晶粒色差(Color Aberration)影像處理、晶粒定位、瑕疵特徵擷取,以及瑕疵分類;所檢測之晶粒瑕疵依區域可分為外型瑕疵(晶粒表面崩損)、發光區瑕疵(發光區表面顏色不一致,發光區破損)與電極區瑕疵(電極刮傷,電極汙染,電極無針痕,Finger斷裂)三個部分。
本研究利用形狀向量相關係數法的模板匹配(Shape-Base Template Matching)進行色差影像晶粒定位,並利用計算出的形狀相關係數做為特徵,對表面結構毀損的晶粒加以區分。晶粒定位後利用高斯平滑最小門檻值分割法(Gaussian Smoothing Minimal Threshold)、Otsu門檻值分割法(Otsu Threshold Segmentation Method)、型態學與影像相減等技術進行瑕疵標記,並計算瑕疵特徵,包含二值化特徵與灰階值特徵。最後利用田口方法(Taguchi Methods)結合主成分分析(Principal Component Analysis, PCA)進行特徵值分析,縮減分類的資料量與維度,以分析後的瑕疵特徵做為決策樹支持向量機(Decision Tree Support Vector Machine, DTSVM)分類模組的訓練依據,建立最佳化多類支持向量機分類模組(Multi-Class Support Vector Machine Model)針對電極區與發光區進行微瑕疵細分,並與傳統二元化結構支持向量機、類神經網路等分類器比較。結果顯示,本研究之檢測系統整體辨識率在96%以上且500顆晶粒瑕疵分類速度為3秒,驗證本研究系統有效定位晶粒以及獲得可靠瑕疵資訊,成功克服自動化瑕疵檢測系統在色差影像上的不足,建構出即使受到干擾仍然具有強健性的檢測流程,而且對瑕疵分類具有快速、高精準度與穩定度等特點,能有效應用於大量生產之發光二極體精密檢測,取代人工目檢來節省人力成本。
This study entitled “The Development and Research on Using Image Processing Technology and Optimization of Multi-Class Support Vector Machines in Light-emitting Diode (LED) Chip Surface Color Aberration and Micro-defect Detection System” aimed to propose the optimization design of the automatic system at the detection stage of the chip manufacturing process, covering LED chip color aberration image processing, chip location, defective feature extraction. Based on the defect, the defects of the tested chip can be divided into shape defects (chip surface collapse and loss), the light emitting area defects (light emitting area surface color differences, the light emitting area damage) and the electrode area defects (electrode scratching, electrode contamination, lack of pin marks in the electrode, finger fracture) by area.
This study used the shape-base template matching for color aberration image chip location, and used the calculation result of the shape correlation coefficient as the feature to distinguish the chips of damaged surface structure. After the chip location, this study employed the Gaussian smoothing minimal threshold segmentation approach, Otsu threshold segmentation method, and morphology and image subtraction to mark the defects, and calculate the defective features, including the binarized features and the grayscale features. Finally, the Taguchi method and the principal component analysis method were applied to analyze the defective features, reduce the classification data amount and dimensions. With the analyzed defect characteristics as the training basis for decision tree support vector machine, this study established the optimization multi-class support vector machines classification module to further distinguish the micro defects in the electrode area and the light emitting area. Comparison was made with the traditional binary structure support vector machines, neural network classifiers. The results suggested that the overall recognition rate of the inspection system can be more than 96% and the classification of defects in 500 chips takes only 3 sec, verifying that the system can effective locate the chip and obtain reliable defect information. The design can successfully overcome the shortcoming of the automatic defect inspection system in terms of color aberration image to establish a robust inspection procedure even under interference. Meanwhile, the defect classification is characterized by fast speed, high accuracy and high reliability. Therefore, it can be effectively applied in the precision inspection of mass-produced LED chips to replace the existing human eye inspection to save labor costs.
摘要 I
Abstract III
誌謝 V
目錄 VII
圖目錄 XI
表目錄 XIV
圖表目錄 XVI
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 文獻回顧 3
1.3.1 發光二極體瑕疵檢測系統 3
1.3.2 影像定位與瑕疵標記 5
1.3.3 分類器最佳化理論 6
1.3.4 瑕疵分類 7
1.4 論文架構 9
第2章 檢測機台與量測系統 12
2.1 影像擷取系統 12
2.2 作業系統與開發軟體 14
第3章 發光二極體介紹 17
3.1 發光二極體的發光原理與結構 18
3.2 發光二極體的製程 20
3.3 發光二極體的優點及應用 21
第4章 瑕疵定義與影像色差 23
4.1 瑕疵定義 23
4.2 瑕疵成因 29
4.3 影像色差 31
第5章 研究方法理論 33
5.1 影像資料結構 33
5.1.1 次像元精度輪廓 33
5.2 幾何轉換 34
5.2.1 仿射轉換 34
5.2.2 影像轉換 35
5.3 邊緣擷取 35
5.3.1 邊緣定義 35
5.3.2 一維邊緣擷取 37
5.3.3 二維邊緣擷取 38
5.4 模板匹配 39
5.4.1 影像金字塔 40
5.4.2 次像元精度匹配 41
5.4.3 旋轉與縮放 41
5.4.4 強健的模板匹配演算法 42
5.5 影像分割 46
5.5.1 高斯平滑分割法 47
5.5.2 歐蘇法(Otsu) 47
5.6 形態學 50
5.6.1 開放與封閉 51
5.6.2 洞的填充 51
5.7 影像相減 52
5.8 特徵擷取 52
5.8.1 二值化特徵(幾何特徵) 53
5.8.2 灰階值特徵(紋理特徵) 54
5.9 田口方法 57
5.10 主成分分析 60
5.11 支持向量機 63
5.12 決策樹 67
5.13 監督式倒傳遞類神經網路 71
第6章 實驗結果與方法驗證 74
6.1 晶粒定位 75
6.2 影像檢測流程 80
6.2.1 色差二值化分析 80
6.2.2 背光源影像檢測流程 83
6.2.3 正光源影像檢測流程 88
6.3 瑕疵影像特徵分析 91
6.3.1 發光區田口方法與主成分分析 91
6.3.2 電極區田口方法與主成分分析 93
6.4 瑕疵分類 103
6.4.1 發光區瑕疵分類 103
6.4.2 電極區瑕疵分類 106
6.5 結果討論 116
第7章 結論與未來展望 118
7.1 結論 118
7.2 未來研究方向 120
參考文獻 121
作者簡介 127
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