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研究生:蕭凱駿
研究生(外文):Kai-Chun Hsiao
論文名稱:應用集成方法於半導體蝕刻製程晶圓分類之研究
論文名稱(外文):Application ensemble method for wafer classification in semiconductor etching process
指導教授:王福琨王福琨引用關係
指導教授(外文):Fu-Kwun Wang
口試委員:李文義歐陽超朱道鵬
口試日期:2019-06-20
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:76
中文關鍵詞:數據不平衡隨機森林多層感知器集成方法
外文關鍵詞:Imbalanced DataRandom forestMultilayer perceptronEnsemble method
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近年來物聯網、工業4.0、大數據分析、智慧製造蓬勃發展,加速了預測與健康管理的普及。在半導體的產業中,任何的故障造成生產線突然停線所造成的所失,都是難以估計的,由於半導體製造過程愈趨複雜,傳統檢驗控制方式已無法滿足製程需求,故各大半導體製造廠投入龐大的資源開發新的監控系統,而先進製程控制技術已經被應用在製程監控系統上。本研究的問題是對蝕刻製程晶圓之分類進行研究。因此本研究主要目的是提供一個集成學習方法(Ensemble method),主題注重於使用深度學習多層感知器(Multilayer Perceptron, MLP)與機器學習隨機森林(Random Forest, RF)分類技術方式,對分類預測結果表現的比較。採用源自Eigenvector Research, Inc德州儀器公司(Texas Instruments Inc.)之設備編號SEMTECH J-88專案的公開數據集,資料中含有129個晶圓資料,由108個正常的晶圓和21個異常的晶圓組成,經由合成少數過取樣技術(Synthetic Minority Oversampling Technique, SMOTE)解決數據不平衡資料,然後再進行RF與MLP分類之後,運用Ensemble結合兩種分類器,使用分類的評判標準為TPR、FPR、AUC,最後Ensemble之TPR、FPR、AUC值分別為99.85%、3.67%、99.94%皆優於Moyne et al.(2017)提到的Whole Trace Stats、Manual Windowing、Semi-automated FD之TPR、FPR、AUC。
In recent years, the Internet of Things, Industry 4.0, Big Data and Analytics, and Smart Manufacturing Technologies have flourished, accelerating the popularity of Prognostics Health Management. In the semiconductor industry, any failure caused by the sudden stop of the production line is difficult to estimate. Due to the increasingly complex semiconductor manufacturing process, the traditional inspection control method can’t meet the process requirements, so the major semiconductor manufacturers have invested huge resources to develop new monitoring systems, and advanced process control technology has been applied to process monitoring systems. The problem in this study was to study the classification of etching process wafers. Therefore, the main purpose of this study is to provide an Ensemble method base on Multilayer Perceptron (MLP) and Random Forest (RF) classification techniques to get prediction results. A public data set from the SEMTECH J-88 project from Eigenvector Research, Inc. Data set includes 129 wafers, 108 normal wafers and 21 anomalies Wafer composition, through the synthesis of a few oversampling techniques (SMOTE) to solve the data imbalance data, and then after the random forest and multi-layer perceptron classification, Ensemble combined with two classifiers, using the classification criteria TPR, FPR, AUC, and finally Ensemble's TPR,FPR and AUC values are 99.85%, 3.67%, and 99.94%, respectively, which are superior to Whole Trace Stats, Manual Windowing, Semi-automated FD, mentioned by Moyne et al. (2017).
摘要 .......................................................................................................................... I
Abstract .................................................................................................................... II
致謝 ........................................................................................................................ III
目錄 ........................................................................................................................ IV
圖目錄 .................................................................................................................... VI
表目錄 ................................................................................................................... VII
第一章 緒論 ........................................................................................................... 1
1.1研究背景 ....................................................................................................... 1
1.2研究動機 ....................................................................................................... 2
1.3研究問題與目的 ........................................................................................... 2
1.4研究範圍與限制 ........................................................................................... 3
1.5研究流程 ....................................................................................................... 3
第二章 文獻探討 ................................................................................................... 5
2.1半導體產業 ................................................................................................... 5
2.2 蝕刻製程 ....................................................................................................... 7
2.3 數據不平衡 ................................................................................................... 8
2.4 檢測與分類的方法 ....................................................................................... 9
2.4.1支持向量機 ................................................................................................ 9
2.4.2 隨機森林 .................................................................................................. 14
2.4.3 引導隨機森林 .......................................................................................... 22
2.4.4 加權子空間隨機森林 .............................................................................. 23
2.4.5多層感知器類神經網路 .......................................................................... 26
2.5混淆矩陣 ..................................................................................................... 31
第三章 研究方法 ................................................................................................. 34
3.1 合成少數過取樣技術 ................................................................................. 34
3.2 集成學習方法 ............................................................................................. 34
3.3 隨機森林 ..................................................................................................... 37
3.4多層感知器 ................................................................................................. 37
第四章 案例分析 ................................................................................................. 39
4.1半導體蝕刻製程 ......................................................................................... 39
4.2資料介紹 ..................................................................................................... 39
4.3.1支持向量機 .............................................................................................. 42
4.3.2 隨機森林 .................................................................................................. 43
4.3.3加權子空間隨機森林 .............................................................................. 44
4.3.4引導隨機森林 .......................................................................................... 45
4.3.5多層感知器 .............................................................................................. 46
4.3.6集成方法 .................................................................................................. 47
第五章 結論 ......................................................................................................... 50
參考文獻 ............................................................................................................... 51
附錄 ....................................................................................................................... 55
附錄A 機器學習 .............................................................................................. 55
附錄B 多層感知器 .......................................................................................... 61
附錄C 多層感知器與隨機森林 ...................................................................... 64
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網路文獻
Eigenvector Research, Inc。網址:https://eigenvector.com/resources/data-sets/。上網時間:2018-08-12(1999)
科普雜誌。網址: https://kopu.chat/2017/03/24/ic-terms/。上網時間:2019-04-25(2017)
產業價值鏈資訊平台。網址:https://ic.tpex.org.tw/introduce.php?ic=D000。上網時間:2019-05-01(2019)
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