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研究生:顏宏叡
論文名稱:應用類神經網路建立半導體機台生產績效預測系統之研究
論文名稱(外文):A Study on the Construction of a Production Performance Prediction System for Semiconductor Machine with Artificial Neural Network
指導教授:劉志明劉志明引用關係
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:2007
畢業學年度:95
語文別:中文
論文頁數:55
中文關鍵詞:半導體預測類神經網路
外文關鍵詞:semiconductorpredictionNeural Networks
相關次數:
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半導體製造現場的重要生產指標為在製品量與流動量,現場管理者藉由生產指標的變動情形了解製造現場的生產狀況與進度,並根據過去的經驗做出適當的判斷,進行相對應的決策。但是這樣的做法,只能做到瞭解已經發生的「歷史」或稱為「實績」,並無法在事前防範異常的發生。如果能夠提出一個預警機制,事先預測未來生產績效的可能變化,就可以依據預測結果得知機台的生產績效變差,事先做出預防措施,以防止事態變嚴重,進而促進系統整體績效的提升。
因此本研究探討如何預測半導體廠機台生產指標中的在製品與流動量變動情況,做為管理者進行現場資料調配與控制的參考依據。本研究利用類神經網路為工具,以國內某半導體廠的資料進行實證研究,參考過去文獻以及專家建議的變數建立預測模型,並且透過網路修剪的過程,逐次刪除影響較小的變數,以找出最佳變數組合的類神經網路模型。實驗的結果顯示,本研究所提出的類神經網路建構方法能夠有效的預測生產指標未來一天的變動情況,在製品數量的預測誤差為7.33%,流動量的預測誤差為7.63%,可以達到實際應用上的參考依據。
WIP and Move are two important production indices for a semiconductor manufacturing fab. The shop managers need to know the production condition and progress based on the variation of these indices and make proper judgment and corresponding decisions based on their experiences. However, this kind of execution provides only understanding of history or facts that happened before. In order to predict possible production variation; to prevent problems getting worse; and to facilitate the whole system performance, it is necessary to have an early warning mechanism that can predict possible future production deviations.
Therefore this study focuses on how to predict the variation of WIP and Move to provide managers insights for material flow and control. The neural networks techniques are used to verify the proposed prediction model with data from one local semiconductor factory. The variables of the neural prediction model are chosen by reference to literature and consultation of experts. Through the process of net pruning to delete variables which have minor influence one by one to find the neural networks model with the best variables combination. The results showed the proposed prediction model can effectively predict the variation of production indices one day ahead, and had its practical contribution. The prediction error of WIP is about 7.33% and the prediction error of Move is about 7.63%.
誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍、假設與限制 4
1.4 論文架構 4
第二章 文獻探討 7
2.1 生產規劃系統架構 7
2.2 績效預測模式介紹 10
2.2.1 系統模擬 10
2.2.2 時間序列 11
2.2.3 複迴歸模型 12
2.2.4 類神經網路 12
第三章 建構預測模型 25
3.1 選擇變數 25
3.2 建立類神經網路模型 27
3.2.1 決定倒傳遞網路模型結構 27
3.2.2 調整網路模型參數 29
3.2.3 尋找最佳模型 30
3.3 建立複迴歸模型 31
3.3.1 複迴歸模式 32
3.3.2 複迴歸模式之估計量 33
3.3.3 複迴歸模式之驗證 34
3.3.4 選取預測變數的方法 37
3.4 比較預測誤差之結果 38
第四章 案例研究 40
4.1 案例公司介紹 40
4.2 變數選擇 40
4.3 建立倒傳遞網路模型 41
4.3.1 決定倒傳遞網路模型結構與模型參數 42
4.3.2 尋找最佳模型 43
4.4 複迴歸分析 45
4.4.1 複迴歸分析之WIP預測模型 45
4.4.2 複迴歸分析之Move預測模型 46
4.5 結果分析 48
4.5.1 預測誤差分析結果之比較 48
4.5.2 評估最佳組合運用於其他機台之預測結果 48
第五章 結論 50
5.1 論文結論 50
5.2 未來研究方向 50
參考文獻 52
英文部分 52
中文部分 55
英文部分
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中文部分
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[32] 陳國玄,「人工神經網路與統計方法應用於台灣上市公司電子指數預測與分類之研究」,國立成功大學碩士論文,民國九十二年。
[33] 蔡宗憲,「類神經網路模式於短期列車旅運量需求預測之應用」,國立成功大學博士論文,民國九十五年。
[34] 蘇朝墩,「類神經網路模式與應用---上課補充教材」,清華大學工工系,台灣,民國九十五年。
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[36] 陳順宇、鄭碧娥,統計學,華泰書局,三版,台北,民國八十七年。
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