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研究生:王國興
研究生(外文):KAUNG-SHING WANG,
論文名稱:機器視覺應用於影像感測元件封裝製程中之玻璃蓋片瑕疵檢測與分類
論文名稱(外文):Machine Vision Applied to Glass Cover Chip Defect Detection and Classification in the Image Sensor Packing Process
指導教授:郭中豐郭中豐引用關係
指導教授(外文):Chung-Feng Jeffrey Kuo
口試委員:郭中豐
口試日期:2012-07-28
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:123
中文關鍵詞:影像處理瑕疵檢測最小矩形區域決策樹適應性模板遮罩
外文關鍵詞:Image ProcessingDefect InspectionMinimum Rec
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本研究主要針對影像感測元件封裝製程中之玻璃蓋片瑕疵進行檢測與分類。玻璃蓋片檢測分為兩個部分,分別為非光阻區檢測與光阻區檢測。非光阻區瑕疵有暗點、溶液殘留與刮傷,而光阻區有光阻殘缺與崩邊等瑕疵。
本研究首先針對非光阻區部分以平滑濾波的方式保留影像光源分佈並大幅降低瑕疵信號產生一平滑影像,接著利用原影像與其相減
,去除光源分佈並留下瑕疵影像,再搭配影像二值化、形態學(Morphology),進行瑕疵檢測(Defect Detection)。此檢測流程經驗證可成功取得影像上之溶液殘留、暗點與刮傷瑕疵,將檢測出之瑕疵尺寸與原影像瑕疵比較後顯示此流程對於低對比度瑕疵如溶液殘留,有良好的效果,而對於高對比度瑕疵如暗點,可明顯的顯示產生瑕疵放大的現象,因此本研究再提出以最小矩形區域(Bounding Box)的方式取出高對比度瑕疵,並使用影像伸張強化提高對比度後,進行影像再分割,成功的取得與原始瑕疵影像尺寸相近的二值化瑕疵影像,達到微觀的玻璃蓋片瑕疵檢測,以實際標準的樣本測試發現可檢測出最小面積2.5μm 2.5μm的暗點,驗證本研究成果符合業界對玻璃蓋片所制定之5μm 5μm瑕疵大小檢測規範。接著對檢測到的瑕疵進行影像特徵擷取,主要提取特徵有平均灰階值、面積、似圓性、灰階均勻度,並利用決策樹J48演算法進行辨識,其整體辨識率達98.34%。最後針對光阻區部分,以適應性模板遮罩(Adaptive Template Mask)的方式搭配Otsu法與交集運算、差集運算,可達到理想的光阻殘缺與崩邊瑕疵檢出效果,且利用決策樹(Decision Tree)J48演算法對其進行辨識
,可達100%之辨識率。
在本研究中,處理單張像素大小為2592 1944之影像,其非光阻區檢測時間為0.98秒,光阻區檢測時間為0.81秒,總處理時間為1.79秒,解決了人力檢測上耗時的問題,此一系統性的瑕疵檢驗方式能全面的應用在影像感測元件封裝製程中玻璃蓋片瑕疵檢測與分類,提高產品之良率。
This study focused on the glass cover chip defect detection and classification in the image sensor packing process. Glass cover chip defect detection can be divided into two parts, namely, the non-photoresist zone detection and photoresist zone detection. Defects in the non-photoresist zone include dark spots, solution residuals and scratches, and the photoresist zone has defects including photoresist deformity and chipping.
Regarding the non-photoresist zone, this study used the smooth filtering approach to retain the image light source distribution and considerably reduce defect signals to generate a smooth image. Then, by subtracting it with the original image, the light source distribution was eliminated, while the defect image was retained for defect detection using binarization and morphology. The results confirmed that the detection process can be successfully applied in getting the image defects of solution residuals, dark spots and scratches. The comparison of the detected defect dimensions and the original image defects suggested that the process has very good effects on low contrast defects, such as solution residuals. Regarding high contrast defects such as dark spots, the amplification of defects can be apparently highlighted. Therefore, this study determined the high contrast defects using the minimum rectangular regions, and used the image zooming to enhance the contrast for image re-segmentation. The binary defect image in dimensions similar to the original defect image sizes was obtained for micro glass cover chip defect detection. The test on real standard samples found that it can detect the dark spot of minimum area at 2.5μm, which met the industrial standards of the glass cover chip defect sized at 5μm. This stud also extracted the image characteristics of the detected defects. The main characteristics of capturing included the average grey scale value, area, circularity, gray-scale uniformity. The decision-making tree J48 algorithm was used for recognition and the overall recognition rate reached 98.34%. Finally, regarding the photoresist zone parts, the adaptive template mask approach, coupled with the Otsu approach, the intersection computing, and the difference set computing, was used to realize the ideal photoresist deformity and chipping defect detection effects. The decision making tree J48 algorithm was used for recognition, and achieved a recognition rate of 100%.
In this experiment, an image of pixel sized 2592*1944 was processed. The non-photoresist zone detection time was 0.98 sec, the photoresist zone detection time was 0.81 sec, and the total processing time was 1.79 sec. Thus, the time consuming problem of manual detection was solved. The systematic defect detection approach could be fully applied in the detection and classification of glass cover chip defects in the image sensing component packaging process to improve product yield.
中文摘要 I
英文摘要 III
誌謝 VI
目錄 VIII
圖索引 XII
表索引 XV
第1章 緒論 1
1.1 研究動機與背景 1
1.2 研究範圍與目標 3
1.3 文獻回顧 6
1.3.1 影像感測元件之玻璃蓋片瑕疵檢測 6
1.3.2 影像分割 9
1.3.3 瑕疵分類理論 10
1.4 論文架構及研究流程圖 12
第2章 影像擷取系統與影像處理分類軟體 15
2.1 影像擷取系統 15
2.2 電腦硬體設備與作業系統 18
2.3 程式開發軟體 18
2.4 瑕疵分類軟體 19
第3章 數位影像感測器介紹 21
3.1 影像感測元件結構 26
3.2 玻璃蓋片介紹 28
3.3 定義瑕疵種類 32
第4章 研究方法相關理論 35
4.1 數位影像處理技術 35
4.1.1 數位影像表示方式 35
4.1.2 均值濾波 36
4.1.3 中值濾波 39
4.1.4 影像相減 41
4.1.5 直方圖等化 44
4.1.6 影像對比伸張強化 47
4.1.7 正交投影 47
4.1.8 影像遮罩 49
4.1.9 影像分割 51
4.1.9.1 統計式閥值決定法 53
4.1.9.2 修正型疊代法 55
4.1.10 形態學 58
4.1.10.1 連通物件標記法 58
4.1.10.2 侵蝕 60
4.1.10.3 膨脹 62
4.1.10.4 閉合運算與斷開運算 64
4.2 影像特徵 65
4.2.1 影像幾何特徵 65
4.2.2 最小矩形區域 66
4.2.3 面積與周長 66
4.2.4 形狀飽和度 67
4.2.5 灰階均勻度 67
4.3 分類器理論 69
4.3.1 決策樹基本理論 69
4.3.2 決策樹演算法 71
4.3.3 決策樹的修剪 75
第5章 實驗規劃與方法驗證 78
5.1 玻璃蓋片非光阻區瑕疵檢測 80
5.1.1 非光阻區影像前處理 80
5.1.2 玻璃蓋片非光阻區瑕疵分類 88
5.2 光阻區檢測瑕疵檢測 91
5.2.1 光阻殘缺瑕疵檢測 92
5.2.2 崩邊瑕疵檢測 94
第6章 結論與未來方向 97
6.1 結論 97
6.2 未來研究方向 99
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