機器視覺之馬可夫算程－半導體銲線製程之應用

Markovian Algorithms for the Gray Thresholding of Machine Vision in IC Wire-Bonding Process

陳文魁(Wen-Kuei Chen)；蔡裕正(Yu-Cheng Tsai)

IC銲線 ； 銲墊定位 ； 二值影像 ； 門檻灰度 ； 馬可夫算程 ； IC wire-bonding ； Pad positioning ； Binary image ； gray threshold ； Markovian algorithms

品質學報

13卷1期（2006 / 03 / 01）

1 - 10

繁體中文

積體電路封裝品質的良窳，銲線是一關鍵製程。目前IC封裝業的銲線機已全面自動化，同時也配備了機器視覺系統。在晶墊間接定位的視覺系統環境，銲線的生產力，大量依賴圖鑑教讀和圖樣辨識的科技及技術。然而，縱使視覺系統善於辨識，封裝製程的銲線能力，仍然與晶體數位化影像的「清晰程度」息息相關。晶體二值影相是從按由數值影像轉換而來。就技術而言，遑論整批，即使同片晶圓上各粒晶體其影像亦不盡相同，導致現有的各種最佳門檻技術都不適用於IC封裝產業。探索影像處理的機遇本質，運用馬可夫鏈之狀態和遷移概念，經由辨識編態與門檻搜尋方式的設計，本論文發展出乙套質優的馬可夫算程。對IC封裝業的銲線製程，該算程除了能輔助視覺系統提高辨識生產力外，尚可因銲墊定位準度的改善，而提昇銲線的品質能力。

Wire-bonding is one of the critical processes that determine the quality of IC packaging. Nowadays outstanding wire-bonders are all automatic and they are equipped well with machine-vision either. With the indirect pad-positioning system, the bonding productivity depends heavily upon technology as well as techniques in pattern recognition. Even if the vision system is good at recognizing, the bonding capability is still strongly related to the ”clearness” of the binary die image. The binary die image in the wire bonding process is converted from its digital die image according to the gray threshold. Technically, for the determination of gray threshold, all optimization methods existing are not appropriate for the IC packaging industry. The reason is that the digital image of each individual die varies at all, even of the same wafer instead of the same lot. In order to overcome the aforementioned positioning problem, this paper is to explore the stochastic features of the die images, and then to develop some robust Markovian algorithms aided to pad-positioning both effectively and efficiently.

1. Anderson, TW,Goodman, LA(1957).Statistical Inference about Markov Chains.Annals of Mathematical Statistics,28,89-110.
2. Brink, AD(1989).Gray-Level Thresholding of Image Using A Correlation Criterion.Pattern Recognition Letters,9,335-341.
3. Doyle, W.(1962).Operation Useful for Similarity-Invariant Pattern Recognition.J. Assoc. Compute.,march,259-267.
4. Fu, K. S.(1982).Pattern Recognition for Automatic Visual Inspection.IEEE Computer,Dec.,34-40.
5. Kaper, J. N.,Sahoo, P. K,Wong, A. K. C.(1985).A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram Computer Vision.Graphics, and Image Processing,29,273-285.
6. Kashioka, S.,Masakazu, E.,Sakamoto, Y.(1976).A Transistor Wire-Bonding System Utilizing Multiple Local Pattern Matching Techniques.IEEE Transactions and SMC,SMC-6(8)
7. McVey, E. S.,Vantol, A.(1979).An Experimental Printed Circuit Board Drilling System Automated By Pattern Recognition.Pattern Recognition Society.
8. Otsu, N.(1979).A Thresholding Selection Method From Gray Level Histograms.IEE Transactions and SMC,SMC-9(1),62-66.
9. Prewitt, J. M. S.,Mendelsohn, M. L.(1966).The Analysis of Cell Images, Ann.New York Academic Science,128
10. Roland, T. C.(1982).Automated Visual Inspection Techniques and applications: A Bibliography.Pattern Recognition,15(4),343-357.
11. Ross, S. M.(1983).Stochastic Process.台北市:開發圖書有限公司.
12. Sahoo, P. K.,Soltani, S.,Wong , A. K. C.,Chen , Y. C.(1988).A Survey of Thresholding Techniques.Computer Vision, Graphics, and Image Processing,41,233-260.
13. Tsai, W.H.(1985).Moment-Preserving Thresholding: A New Approach.Computer Vision, Graphics, and Image Processing,29,377-393.
14. 工研院電子所(1997)。半導體工業年鑑－IC。台北:
15. 王魁生編(1994)。品管檢測技術與校正實務。台北:中華民國品質管制學會。
16. 吳玲生(1991)。實用影像處理。台北:建興出版社。
17. 林長壽(1991)。作業研究－應用及算法。台北:中央圖書出版社。
18. 侯東旭、郭溫良(1994)。自動化檢驗系統績效之評估研究。第九屆全國技術及職業教育研討會工業類IV
19. 陳王政(1993)。碩士論文(碩士論文)。國立成功大學航空測量研究所。
20. 衛祖賞(1990)。電腦視覺在工業上之應用。第五屆全國技術及職業教育研討會論文集