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研究生:陳立揚
研究生(外文):Li-Yang Chen
論文名稱:結合信心再標記技術之複合式晶圓缺陷模式辨識架構
論文名稱(外文):Compositive Framework for Wafer Pattern Recognition with Confidence Relabeling Technique
指導教授:李淑敏李淑敏引用關係
指導教授(外文):Li, Shu-Min
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:76
中文關鍵詞:資料模型監督式框架啟發式演算法缺陷模式辨識半導體缺陷信心學習特徵擷取多重缺陷模式
外文關鍵詞:data modelsupervised frameworkheuristic algorithmdefect pattern recognitionsemiconductor defectsconfidence learningfeature extractionmulti-defect pattern
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  • 被引用被引用:0
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  本研究提出一套自動化、一致性高且快速的晶圓模式辨識框架,可以提升錯誤診斷率、進行根本原因分析(Root Cause Analysis)提高良率並降低晶圓缺陷辨識中的昂貴人力(Manpower)成本、避免人工辨識之不一致等問題。
  本研究結合啟發式(Heuristics)演算法與監督式(Supervised)機器學習方法,共分為四個部分。第一部分為特徵提取,旨在產生能辨識位置差異性(Location Variation)、角度差異性(Rotation Variation) 、尺寸差異性(Size Variation) 、形狀差異性(Shape Variation)與密度差異性(Density Variation)等各種差異性(Variation)影響的特徵。第二部分是建構複合式重新標記架構,透過核密度估計(Kernel Density Estimation)與信心學習(Confidence Learning)技術,以最小的人力成本將標記錯誤的資料重新標記(Relabel)。在第三部分,我們使用已修正的資料集透過監督式學習中的XGBoost方法訓練,建構出能辨識每種缺陷模式的機器學習模型(Machine Learning Model)。在第四部分,啟發式演算法將配合訓練好的機器學習模型對輸入的晶圓資料進行缺陷模式辨識。
  本研究找出並修正了WM-811K已標記資料中的10,249個不當標記資料;對再標記後的資料集可以達到平均95.33%的F1-score;提出的架構亦具備擴展性,對於未來新的資料所產生的新種類缺陷模式與多種混和型缺陷模式(即多缺陷模式)也能進行辨識。
  This study proposes an automated, consistent, and fast wafer pattern recognition framework that can improve error diagnosis, perform Root Cause Analysis to improve yield, reduce expensive manpower costs in wafer defect recognition, and avoid inconsistencies in manual recognition.
  This study combines a heuristic algorithm and a supervised machine learning method in four phases. The first phase is feature extraction, which aims to generate features that can recognize location variation without being affected by rotation variation. The second phase is a composite relabeling framework that uses Kernel Density Estimation and Confidence Learning techniques to relabel mislabeled data in the dataset with minimal labor cost. In the third phase, we use the corrected dataset to train several Machine Learning Models that can recognize each defect pattern through the XGBoost method. In the fourth phase, the heuristic algorithm is used with the trained Machine Learning Model to identify the defect patterns on the input wafer data.
  The proposed method identified and corrected 10249 erroneous labels in the WM-811k labeled data; an average F1-score of 95.33% could be achieved for the relabeled data set. The proposed architecture is also scalable to recognize new types of defect patterns generated by new data in the future.
論文審定書 i
摘要 ii
Abstract iii
圖目錄 vi
表目錄 viii
第1章 簡介(Introduction) 1
1.1 晶圓缺陷模式分類的重要性 1
1.2 WM-811K資料集 2
1.3 研究動機 3
1.4 主要貢獻 5
第2章 相關研究(Related Work) 6
2.1 使用機器學習進行晶圓缺陷模式辨識 6
2.2 噪聲資料對機器學習的影響 7
第3章 問題定義(Problem Definition) 8
3.1 晶圓缺陷模式及辨識難點 8
3.1.1 晶圓缺陷模式多樣性 8
3.1.2 WM-811K中的不當標記 10
3.2 問題描述及定義 11
第4章 Wafer Confidence Learning方法論 (Methodology) 12
4.1 階段一資料選擇與特徵提取 13
4.1.1 資料選擇(Data Selection) 13
4.1.2 特徵提取(Feature Selection) 14
4.2 階段二資料重新標記(Data Relabeling) 17
4.2.1 階段二次階段一以良率之高斯核密度估計過濾Near-Full 19
4.2.2 階段二次階段一使用人工方式過濾Scratch資料 21
4.2.3 階段二次階段一以集中值過濾Absolute-None資料 22
4.2.4 信心不當標記過濾與再標記 25
4.2.4.1 階段二次階段二Nonpattern之信心不當標記過濾與再標記 27
4.2.4.2 階段二次階段三剩餘缺陷模式之信心不當標記過濾與再標記 29
4.3 階段三架構訓練(Framework Learning) 34
4.3.1 依標記挑出Near-Full資料及取得閾值 35
4.3.2 依標記挑出Scratch資料 35
4.3.3 挑出Absolute-None資料及取得閾值 35
4.3.4 訓練各缺陷模式辨識模型 35
4.4 階段四(Phase IV)預測資料(Prediction) 36
4.4.1 使用閾值過濾Near-Full 37
4.4.2 啟發式Scratch辨識演算法—Scratch Marker 37
4.4.2.1 Scratch Marker階段一區域霍夫掃描 38
4.4.2.2 Scratch Marker階段二候選線段過濾 40
4.4.2.3 Scratch Marker階段三候選線段合併 43
4.4.2.4 Scratch Marker最終檢查(Final Verification) 46
4.4.3 使用閾值過濾Absolute-None 47
4.4.4 使用機器學習模型預測剩餘缺陷模式 47
第5章 實驗成果(Experiment Result) 48
第6章 結論(Conclusion) 59
參考文獻 61
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