臺灣博碩士論文加值系統

IC構裝的技術不斷演進，在晶片與基板的接合方式中，TAB製程為許多連續生產製程所採用。某廠商對於TAB表面線路瑕疵檢測是採用半自動化的檢測，目前，廠商所面臨到的問題有三：一是TAB線路表面有異物存在，增加了AOI機台檢測的誤判率；二是AOI檢測機台的內建檢測演算法之適用性低；三是人力的使用。本研究將應用機器視覺技術於廠商在進行TAB表面線路瑕疵檢測時遇到的問題做改善，最終的目的，主要是要降低黏附於TAB線路表面的異物對檢測結果所造成之誤判率；其次，則是期望把廠商現有的瑕疵檢測過程改為完全自動化檢驗。
就針對TAB線路表面上黏附異物的問題，本研究提出合併式分割法來進行改善，此方法較傳統之區域成長法使用時所需的參數定義更為簡易，在電腦配備為Pentium Ⅳ 2GHz PC、Win2000作業系統下，使用Matlab 6.5程式語言進行分割作業，平均執行時間減少了14.1097秒，只需3.1883秒即完成分割作業。而由實驗結果得知，合併式分割法的使用，大多數有異物之影像，都能做到完全去除異物的目的，讓有異物之TAB影像的檢測正確率提升83.67％，有助於廠商檢測結果之誤判率降低。
另外，在對於廠商現有AOI檢測機台的內建瑕疵檢測演算法的部分，本研究採用了求取線路邊界之共變異矩陣的做法。以這個方法做瑕疵偵測，其好處是可以不需要參考樣本做比對，且由共變異矩陣的變化找出瑕疵發生處後，還能再進一步的對瑕疵做分類，讓完整的檢測流程都由機器自動化來完成，解決廠商在人力使用上的問題。而這個完整的TAB表面線路之檢測系統，目前能檢測TAB線路上的瑕疵正確率為85.43％。所以，本研究所提出的瑕疵檢測流程與方法，是可以提供給廠商作為解決目前所面臨之三個問題的做法。

TAB processing is a continuous produce processing between IC packaging chips and substrate in the interconnection mode. One of the TAB processing companies has semi-automatic inspection in detecting TAB surface defect. Recently, the company has three problems during TAB processing. First, there are just a few remaining photos resist, and the TAB surface circuits may have some dusts on them. So they would increase the error rate for AOI machine detection. Second, it is not flexible to build a detection algorithm for the AOI machine. Third, the production flow costs a lot of labors. In this research, I’ll use automatic visual approaches to improve those problems above. The main objective is to reduce the error rate and lead the automatic defecting detection achieve more complete.
This research has built a new merged segmentation method to solve the problems about the TAB surface circuit. The new segmentation method has merged the “Region Growing” and “Edge Detection” techniques. It is more useful to define parameters by using the new algorithm than by the traditional region growing method. The presented method spends only 3.1883 seconds in using merged segmentation method. The speed of the program running is also faster than the speed of the traditional region growing method. The result of the image test is that using merged segmentation method can make image more clearly. Besides, the conclusion shows that the error rate have been reduced, and the correct rate is raising to 83.67％.
In addition, this research presents a covariance matrix method to detect surface defect. It can increase flexibility for TAB surface defect system, and one of advantages is that pattern matching is not necessary. After finding defecting location by covariance matrix method, we can continue defecting classification. It can achieve a complete automatic defect detection process, and decrease the necessary about labors. In this paper, we can construct a trend of complete automatic defecting detection process, and it will increase the correct rate of detecting defect up to 85.43％.

書名頁………………………………………………………………………………….I
中文摘要…………………………………………………….………….…………….....II
英文摘要……………………………………………………………….………………III
誌謝……………………………………………………………….………………….IV
目錄……………………………………………………………….…………………V
表目錄……………………………………………………………….……………….VII
圖目錄……………………………………………………………….…………………IX
第一章 緒論…………………………………………………………………………1
1.1 研究背景…………………………………………………………………...1
1.2 研究動機與目的…………………………………………………………...6
1.3 研究流程…………………………………………………………………...7
第二章 文獻探討……………………………………………….…………………...9
2.1 影像分割處理……………………………………………………………...9
2.2 角點特徵萃取…………………………………..………………………...13
2.3 瑕疵分類………………………………………..………………………...15
第三章 研究方法…………………………...……………………………………...17
3.1 完整檢測流程說明……………………………..………………………...17
3.2 影像分割處理……………………………………………………..……...18
3.2.1 TAB表面線路影像分析………………………………………………..18
3.2.2 影像分割方法…………………………………………………...……...19
3.2.2.1 區域成長（Region Growing）法…………………………..………...19
3.2.2.1.1 應用區域成長法於TAB影像之分割處理流程概述……………...25
3.2.2.2 合併式影像分割法…………………………………………………...28
3.2.2.2.1 應用合併式分割法於TAB影像之分割處理流程概述…………...32
3.2.2.3 兩背景影像的分割法………………………………………………...45
3.3 瑕疵偵測與分類………………………………………………………….49
3.3.1 瑕疵偵測………………….……………………..……………………...49
3.3.2 瑕疵分類………………………….……………..……………………...53
第四章 實驗結果與討論……………………………...…………………………...60
4.1 實驗樣本與設備………………………………………..………………...60
4.2 TAB影像的分割結果…………………………………………………….60
4.2.1 區域成長演算法………………………………………………………..60
4.2.1.1 區域成長法之參數設定……………………………………………...60
4.2.1.2 分割成功之影像……………………………………………………...73
4.2.1.3 分割失敗之影像……………………………………………………...78
4.2.2 合併式分割演算法……………………………………………………..80
4.2.2.1 合併式分割法之參數設定…………………………………………...80
4.2.2.2 分割成功之影像……………………………………………………...84
4.2.2.3 分割失敗之影像……………………………………………………...91
4.2.3 分割方法比較與結果分析……………………………………………..92
4.3 TAB影像之瑕疵偵測與分類結果……………………………………….94
4.3.1 瑕疵偵測之參數設定…………………………………………………..94
4.3.2 瑕疵偵測與分類結果…………………………………………………..97
4.4 實驗結果分析與討論…………………………………………………...113
第五章 結論與未來發展…………………...…………………………………….114
參考文獻……………………………………...………………………….…………116

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