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研究生:翁茂虔
研究生(外文):Mao-chian Weng
論文名稱:應用空間檢定與不變性轉換距離於晶圓圖分類問題
論文名稱(外文):Classification of Wafer Bin Maps by Using Spatial Tests and Invariant Transformation Distance
指導教授:鄭順林鄭順林引用關係
指導教授(外文):Shuen-lin Jeng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:統計學系碩博士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:93
中文關鍵詞:馬可夫隨機域(MRF)空間同質伯努立過程(SHBP)晶圓圖(WBM)不變性轉換距離(ITD)分割環繞物件法(PAM)
外文關鍵詞:Markov Random Field(MRF)Spatial Homogeneous Bernoulli Process (SHBP)Partition Around Meloids(PAM)Wafer Bin Map (WBM)Invariant Transformation Distance(ITD)
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  • 下載下載:42
  • 收藏至我的研究室書目清單書目收藏:0
半導體的製造是資本密集的產業並且常牽涉數百道複雜的程序,良率的提升為製造廠增加進競爭力最重要的一環。晶圓圖是提供追溯產品發生異常原因的重要線索,藉由晶圓圖的模型分析得以找出可能發生低良率的原因,例如有問題的機台或異常的製程步驟等。目前半導體廠對於晶圓圖的圖形判斷,大多以人工目視判斷的方式來進行,但由於人為主觀因素與對空間圖形辨識能力的差距,經常造成圖形分類結果的不一致,甚至因此影響問題解決的效率。

有鑑於此,我們提出了一個對於晶圓圖的圖形辨識流程。我們考慮空間同質伯努立過程(SHBP),馬可夫隨機域(MRF),不變性轉換距離(ITD),歐氏距離(UD),分割環繞物件法(PAM)於特殊圖樣的晶圓圖分群。我們建立一套晶圓圖樣辨識程序,希望藉由此流程可以協助工程師結合其相關的領域知識,而縮小事故診斷之範圍,減少決策分析所需要的時間。
Semiconductor manufacturing involves hundreds of complex processes, and is capital intensive. Yield improvement is increasingly important as advanced fabrication technologies are complicated and interrelated. Particularly, wafer bin maps (WBM) that present specific failure patterns provide critical information to track the process problems in semiconductor manufacturing. However, most companies rely on experienced engineers' judgments of the map patterns through eye analysis. Thus, it is time consuming and subjective because of the limited capability of human recognition.

For this reason, we propose a procedure for routinely recognition of wafer bin map to detect the presence of spatial patterns. We consider Spatial Homogeneous Bernoulli Process (SHBP) and Markov Random Field (MRF) models for spatial patterns, Invariant Transformation Distance (ITD) and Euclidean Distance (UD) for dissimilarity measurements and Partition Around Meloids (PAM) for pattern grouping. A hybrid approach for pattern extraction from wafer bin map is built. This procedure can assist the engineers to find the process problems from defective patterns in the WBMs by reducing the problem scope and work time.
1. Introduction 1
1.1 Background 1
1.2 Problem and Approach 5
1.3 Overview 7
2. Literature Review 9
2.1 Wafer Fabrication and CP Test 9
2.2 WBM Classification 10
2.3 Invariant Transformation Distance 13
2.4 Clustering 14
3. Data Exploration 15
3.1 Basic Statistics 16
3.2 Yield Analysis 18
3.3 Wafer Bin Map 21
4. Related Methods 39
4.1 Spatial Test 39
4.1.1 Odds Ratio Test 39
4.1.2 The HNF Test 42
4.2 Enhance the Signal and Remove the Noise 47
4.2.1 King-Move Neighborhood 47
4.2.2 Smooth Interpolating 48
4.3 Invariant Transformation Distance (ITD) 48
4.4 Partitioning Around Medoids (PAM) 51
5. Empirical Study 56
5.1 Spatial Randomness Test 56
5.1.1 Odds Ratio Test 57
5.1.2 HNF Test under SHBP Model 58
5.1.3 HNF Test under MRF Model 59
5.2 De-Noise 60
5.3 Map Clustering 62
5.3.1 Clustering Result 63
6. Conclusion and Future Work 80
6.1 Conclusion 80
6.2 Future Work and Difficulty 81
Appendix 87
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