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研究生:江冠賢
研究生(外文):Kuan-Hsien Chiang
論文名稱:應用資料探勘技術控制PI製程之膜厚變異
論文名稱(外文):A Data Mining Approach to the Control of Thickness Variation for Polyimide Film Manufacturing
指導教授:巫木誠巫木誠引用關係
指導教授(外文):Muh-Cherng Wu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
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:2008
畢業學年度:96
語文別:中文
論文頁數:42
中文關鍵詞:良率改善polyimide film資料探勘1RC4.5
外文關鍵詞:Yield enhancementPolyimide filmData Mining1RC4.5
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對於生產製造業而言,製程良率的高低是導致ㄧ間公司成敗的關鍵因素。台灣某公司的polyimide film製程ㄧ直以來都無法有效控制其產品的膜厚變異,原因在於製造程序的複雜,不論在製程的前段或是後段,都會蒐集產品通過機台而自動化或人工記錄的參數資料,要從這些龐大的資料量中找出影響膜厚變異的關鍵因子,僅靠專業知識及傳統的統計方法是不夠用的。因此本研究利用資料探勘技術結合統計方法去建構一個可以改善製程良率的資料探勘新架構,包含先應用1R找出可疑參數來縮小製程範圍,然後利用相關分析與專業知識發現可控參數,最後建立迴歸方程式找出可控參數的設定值。結果有效的幫助公司找出影響膜厚變異的關鍵因子,使得製程良率從41.7 %提升到61 %,大幅改善約20 %。除此之外,本研究還比較四種1R版本及四種C4.5版本於該資料庫上的表現,結果指出1R不僅可使用在良率改善上,且表現不遜色於C4.5。
Process yield is a very important performance index for manufacturing. In order to enhance yield, process data will be automatically or semi-automatically recorded for diagnosing faults. Many production processes often involve hundreds of process and quality parameters. As a result, finding the root causes of process variation becomes a difficult problem. This study combined data mining techniques and statistical methods to develop a solution framework for identifying the critical process parameters. This framework applied 1R algorithm to identify the clue variables, and then used correlation analysis and domain knowledge to find the to-be-controlled variables. Finally, it utilized regression analysis to set the value of the to-be-controlled variables. We validated this framework with an empirical study about the control of thickness variation for polyimide film manufacturing. Results indicated that the process yield has been improved from 41.7 % to 61 %. Moreover, we compared four versions of 1R with four versions of C4.5, and our initial results show the promise of 1R algorithm to improve the process variation.
中文摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1研究動機 1
1.2問題分析 3
1.3研究方法 5
1.4論文架構 5
第二章 文獻探討 6
2.1資料探勘及其技術 6
2.2 1R及其相關討論 8
第三章 研究架構 11
3.1問題定義 12
3.2資料前置處理 12
3.2.1 資料整合 12
3.2.2 參數縮減 12
3.2.3 資料清理 13
3.3資料探勘 13
3.3.1 資料型態轉換 13
3.3.2 重要參數篩選 14
3.3.3 參數規則評估 14
3.4物理意義評估 15
3.5現場驗證 17
第四章 膜厚變異改善分析與結果 18
4.1問題定義 18
4.2資料前置處理 19
4.3資料探勘 20
4.3.1 資料型態轉換 20
4.3.2 重要參數篩選 20
4.3.3 參數規則評估 21
4.3.4 比較1R與C4.5 21
4.4物理意義評估 22
4.4.1 鄰近參數搜尋 22
4.4.2 相關程度分析 22
4.4.3 物理輔助釋意 25
4.5現場驗證 28
第五章 結論及未來研究方向 31
5.1結論 31
5.1.1 工廠實務 31
5.1.2 學術研究 31
5.2未來研究方向 32
參考文獻 33
附錄一 189個製程參數 35
附錄二 83個製程參數 37
附錄三 四種1R版本參數正確率排序 39
附錄四 參數規則評估結果 40
附錄五 每15分鐘之參數資料 41
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