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研究生:蔡祐翔
研究生(外文):Yu-Hsiang Tsai
論文名稱:使用表徵學習和機器學習方法於晶圓線切割機台之異常偵測
論文名稱(外文):Anomaly detection in wafer wire saw machines using representation learning and machine learning methods
指導教授:洪炯宗洪炯宗引用關係吳立青
指導教授(外文):Jorng-Tzong HorngLi-Ching Wu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:69
中文關鍵詞:異常偵測不平衡資料集機器學習表徵學習
外文關鍵詞:anomaly detectionimbalance datasetmachine learningrepresentation learning
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晶圓線切割機台用於矽晶圓製造的一環,機台的作用是將晶圓切片。然而,當機台無預警地停機或者切割的線鉅斷裂,整批的晶圓將會成為次等品甚至必須拋棄,這將導致成本增加,而這伴隨著一個大挑戰-資料集不平衡問題。正常與異常資料比例懸殊,為21:1。因此提出了一套異常偵測的策略,由三個部分組成:表徵學習、監督式分類器、警報機制來做出最後決策。k-平均演算法與自編碼器之表徵學習法,僅使用正常資料來學習正常的特徵,這不僅解決資料集不平衡的問題,也讓實驗採用的四個監督式學習分類器:隨機森林、單純貝氏分類器、支援向量機器、極限學習機表現得更好,並且設計一套發警報機制有效減少假警報數量。我們評定這套異常偵測策略的好壞是根據一家合作半導體矽晶圓材料製造公司的真實機台收集之資料,在測試資料集達到偵錯率0.57以及錯報率0.10。此外,這套預測系統已經在產線上實裝和測試,我們也提出了最影響模型好壞的原始工程資料。
Wafer wire saw machines are used in a link of silicon wafer manufacturing, that saws wafers into individual die. However, while the machine shutdown or the sawing wire broken unexpectedly, that batch of wafers will be secondary products or wasted wafers leading to cost increase. Also, it comes up with a challenging issue - the imbalance dataset. The ratio of normal and abnormal data is 21:1. Therefore, an anomaly detection strategy is proposed, composed of three parts: representation learning methods, supervised classifiers and alarm rules. K-means clustering and autoencoders are the representation learning methods that learn normal features from normal data only, that not merely solves the imbalanced data challenge, but also helps the 4 experimental supervised classifiers: random forest, Naïve Bayes, support vector machine, extreme learning machine perform better, whereas the alarm rules help reduce false alarm. The anomaly detection strategy is evaluated on two machines from a real semiconductor silicon wafer material manufacturing company, where the catching rate is 0.57 and false alarm is 0.10. Moreover, this predictive system has been implemented and tested in production line, and we put forward the considerable engineering profiles that are highly related to the models.
中文摘要 i
Abstract ii
致謝 iii
Table of Contents v
List of Figures vii
List of Tables viii
Chapter 1. Introduction 1
1.1. Background 1
1.2. Related Works 3
1.3. Motivation & Goal 4
Chapter 2. Materials and Methods 5
2.1. Data 7
2.1.1. Raw Data Pattern 7
2.1.2. Dataset 9
2.1.3. Data Cleaning 10
2.2. Evaluations 13
2.3. Representation Learning 15
2.3.1. K-means Clustering 15
2.3.2. Autoencoder (AE) 16
2.4. Machine Learning Models 18
2.4.1. Random Forest (RF) 18
2.4.2. Naïve Bayes (NB) 19
2.4.3. Support Vector Machine (SVM) 19
2.4.4. Extreme Learning Machine (ELM) 20
2.5. Alarm Rules 21
2.6. Predictive System 23
Chapter 3. Results 24
3.1. Summary of the Results 24
3.2. Results of all Datasets 27
3.3. Important Profiles Analysis 29
Chapter 4. Discussions and Conclusions 30
4.1. Discussions 30
4.2. Conclusions 31
References 32
Appendix 34
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