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研究生:劉巧雯
研究生(外文):Liu Qiao Wen
論文名稱:利用資料挖礦方法建立半導體晶圓圖之分群分類系統
論文名稱(外文):Developing Data Mining Methods for Constructing A Wafer Bin Map Clustering and Classification System
指導教授:簡禎富簡禎富引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:55
中文關鍵詞:晶圓圖資料挖礦類神經網路
外文關鍵詞:waferdata miningartifical neural network
相關次數:
  • 被引用被引用:13
  • 點閱點閱:446
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
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。因此,本研究整合空間統計檢定方法、影像處理技巧與ART類神經網路理論,發展晶圓圖分類的方法。本研究並以利用訪談蒐集出12種晶圓圖像之損壞樣型,以作為對本研究所提出之分群系統與方法的驗證。並同時以某相關之研究論文做分群後的比較,以驗證本研究所提出之方法的優劣。最後,並紀錄其共同表徵及事故原因於晶圓圖庫中,因此一旦有任何製程異常導致特殊晶圓圖出現,便可藉由與共同表徵進行相似度比對,再結合其關連的領域知識,而縮小事故診斷之範圍,減少決策分析所需要的時間。本研究成果可提高晶圓圖判讀的一致性與事故診斷的效率。
Because of the complex fabrication processes and the cost resulted from defects, analyzing wafer bin maps and mining potentially useful information from large such database become increasingly important in both research and application for semiconductor manufacturing. We develop a hybrid approach that combines spatial statistics and neural networks for wafer bin map (WBM) clustering to assist the product engineers in narrowing possible causes of manufacturing defects. We conducted an empirical study in a semiconductor fab and the results showed the practical viability of this approach.
This study proposed a method to train the algorithm and estimate the validity. In particular, we have developed a hybrid approach that integrates the spatial statistics test, cellular neural network (CNN), adaptive resonance theory (ART) neural network and moment invariant (MI) concept for WBM clustering. The proposed method can cluster a large number of WBMs into a number of groups that show specific failure patterns and thus shorten the time and scope for troubleshooting to improve yield. However, ART is an unsupervised neural network without given target value for comparison. Thus, it is difficult to validate the results of WBM clustering. In particular, we design an experiment to train the algorithm. In this study, we designed an experiment with the generated WBMs based on domain knowledge and thus their true categories are available. We verify this approach with different configurations of ART by examining four indexes of clustering results. Then, we thus select optimal parameter configuration of ART for clustering the WBMs in real setting.
摘要………………………………………………………………….……………….i
Abstract………………………………………………………………………………ii
Content………………………………………………………………………………iii
Table List……………………………………………………………………………..v
Figure List ………………………………………………………….………………..vi
Chapter 1 Introduction
1.1 Background, Significance, and Motivation…… ……………………….…. 1
1.2 Research aims……………………………………………………………… 1
1.3 Overview of the paper…………………………………………..………… 2
Chapter 2 Literature Review………………………………………………………… 3
2.1 Semiconductor manufacturing data………………………………..……… 3
2.2 Data mining…………………………………………………..…………… 4
2.3 Wafer map………………………………………………...…..…………… 7
2.4 Cellular neural network………………………………...…………………. 10
2.5 Methodologies for WBM clustering……………………..…..…………… 14
2.5.1 Adaptive resonant theory (ART)…...…………………..…….……. 15
2.5.2 Self-organize feature map (SOFM)………………..……………… 18
2.5.3 Clustering analysis………………………………………………… 23
Chapter 3 Conceptual Framework………………………………..…………………. 23
3.1 Problem structuring……………………………………..………………… 25
3.2 Data preparation……………………………………..………………….…. 25
3.3 Examine Spatial Dependence…………………..………………………… 26
3.4 Eliminate noise effect and improve pattern effect………………………… 28
3.5 Clustering by moment invariant and ART1……………………………...32
3.5.1 Shape clustering by moment invariant…………………………… 32
3.5.2 Pattern position cluster by adaptive resonant theory (ART)……… 33
Chapter 4 Experiment Design………… …………………………………………. 39
4.1 Data preparation….……………………………………..………………. 39
4.1.1 Identify the clustering templates..…..……………………………...39
4.1.2 Experimental data design…………………….…..…..……………. 39
4.2 Criterion for evaluating the clustering result……………………………. 40
4.3 Examine the Relation between Systematic and Random Effects………..42
4.4 Compare with other cluster method…………………………….…….…. 43
4.5 The cluster system of WBMs….………………………..………………. 46
Chapter 5 Conclusion………………… …………………………………………. 49
References………… ………………………………………………………………51
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