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系統識別號 U0026-0812200912123926
論文名稱(中文) 虛擬量測精度精進之研究與實作
論文名稱(英文) Research and Implementation of Virtual-Metrology Precision Enhancement
校院名稱 成功大學
系所名稱(中) 製造工程研究所碩博士班
系所名稱(英) Institute of Manufacturing Engineering
學年度 94
學期 2
出版年 95
研究生(中文) 吳偉民
研究生(英文) Wei-Min Wu
電子信箱 p9693104@mail.ncku.edu.tw
學號 p9693104
學位類別 碩士
語文別 中文
論文頁數 62頁
口試委員 口試委員-楊浩青
口試委員-洪敏雄
口試委員-楊大和
指導教授-鄭芳田
中文關鍵字 虛擬量測、資料前處理、倒傳遞類神經網路、簡易循環式網路、參數鈍化 
英文關鍵字 Simple Recurrent Neural Network (SRN)  Data Preprocess  Virtual Metrology  Parameter Blunting  Back Propagation Neural Network (BPNN) 
學科別分類
中文摘要 倒傳遞類神經網路為目前所知精度較高,且最常被採用作為建構模型之前饋式網路;但其最大之缺點為訓練時間較長,較不易達成即時性之需求。為解決此問題,本研究改採簡易循環式網路。此法可大幅提昇網路運作效率,且不致影響預測精度。參數篩選為資料前處理之重要一環,可有效提昇預測精度及運作效率。然而,為使虛擬量測模型能保有機台原有的特性,所有機台之重要參數均不可輕易刪除。為兼顧精度之提昇,且不刪除所有機台之任一重要參數,本研究提出一套提昇虛擬量測精度之方法與架構,將影響預測精度之參數進行篩選,並以鈍化之方式取代刪除的效用。此作法除能有效提昇預測精度外,亦可避免因刪除參數所造成在虛擬量測運作階段,該參數無法在系統上顯示任何異常資訊,以致機台發生故障時,無法針對該參數進行調機動作之問題,如此可避免或減少因機台異常造成晶圓報廢之損失。本研究採用二個案例製程進行參數鈍化實驗,其結果均顯示參數鈍化對於精度提昇效果比刪除參數更加明顯。為驗證鈍化實驗對於精度提昇確有其顯著效果,本研究利用Statistica 6.0統計軟體進行虛擬量測預測精度可信度檢定,結果顯示在顯著水準α為0.05時,二個案例製程計三種參數鈍化實驗之p-value均小於0.05,表示該鈍化實驗具有顯著性。

英文摘要 Back propagation neural network (BPNN) is one of the best known feedforward neural networks for high precision performance and model establishment. However, the disadvantage is that BPNN requires a tremendous period of training time; thus it cannot fulfill the real-time implementation requirements. To solve the problem mentioned above, in this work, the simple recurrent neural network (SRN) is adopted, which greatly reduces the training time and shows no impact to conjecture precision.
Parameter sifting is essential to data preprocess, which can improve conjecture precision and operating efficiency. However, in order to make the conjecture model keep track of all the variations of equipment properties, any equipment parameter cannot be deleted without thorough considerations. For these two concerns, a parameter-blunting method for virtual-metrology precision enhancement is proposed. By applying this method, those immaterial parameters will be blunted instead of sifting. As such, the effect of parameter sifting remains while maintaining monitoring all of the equipment parameter variations. Two illustrative examples are included in this work. Both of the examples show that the parameter-blunting method is better than the parameter-sifting method for enhancing virtual metrology precision. Statistica 6.0 is adopted to perform tests of significance for the proposed parameter-blunting method concerning the enhancement of virtual metrology precision. With the significance level α being 0.05, the p-values of three parameter-blunting experiments in each of the two examples are all smaller than 0.05. This reveals that the parameter-blunting method has certain significance indeed.

論文目次 中文摘要
英文摘要
致謝

目 錄 i
圖 目 錄 iii
表 目 錄 v

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 研究流程 5
1.4 論文架構 6
第二章 文獻探討與理論基礎 7
2.1 相關文獻探討 7
2.1.1 虛擬量測方法與架構 7
2.2 相關理論基礎 8
2.2.1 類神經網路原理 8
2.2.2 倒傳遞類神經網路 12
2.2.3 簡易循環式類神經網路 13
第三章 資料前處理 15
3.1 資料前處理目的 15
3.2 資料前處理流程與步驟 16
3.2.1 資料收集 16
3.2.2 異常點清除 24
3.2.3 代表值選取 27
3.2.4 資料標準化 32
第四章 研究方法 33
4.1 簡易循環式網路模型建構 33
4.1.1 演算法之建構 36
4.1.2 預測精度評估指標 39
4.2參數鈍化方法 40
4.2.1參數鈍化之目的 40
4.2.2流程與細部步驟 41
4.2.3精度精進可信度檢定 47
第五章 案例分析結果比較與討論 48
5.1 分析結果比較 48
5.2 可信度檢定結果 54
5.3 QPS(BPNN)與SRN模型建構時間之比較 55
第六章 結論 57
6.1 總結 57
6.2 本研究之貢獻 58
6.3 研究限制 58
6.4 未來研究方向 59
參考文獻 60
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