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系統識別號 U0026-0812200914360883
論文名稱(中文) 適用於全廠導入之虛擬量測自動換模機制
論文名稱(英文) Virtual Metrology Automatic Model Refreshing Scheme for Fab-wide Deployment
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所碩博士班
系所名稱(英) Institue of Manufacturing Information and Systems
學年度 96
學期 2
出版年 97
研究生(中文) 邱政瑋
研究生(英文) Cheng-Wei Chiu
電子信箱 p9695405@mail.ncku.edu.tw
學號 p9695405
學位類別 碩士
語文別 中文
論文頁數 56頁
口試委員 指導教授-鄭芳田
口試委員-蘇育全
口試委員-楊浩青
口試委員-洪敏雄
中文關鍵字 複迴歸  虛擬量測  整體相似度指標  類神經網路  自動換模  換模門檻值 
英文關鍵字 multiple regression (MR)  neural network (NN)  Virtual metrology (VM)  automatic model refreshing  global similarity index (GSI)  refresh threshold (RT) 
學科別分類
中文摘要 在半導體和TFT-LCD產業中,為了能自動地將虛擬量測系統導入全廠,本研究提出一個適用於全廠導入之虛擬量測自動換模機制,係將初期所建立之一組的推估模型作為基模,自動移植或複製至其他尚未有推估模型之同型機台中。此外,換模程序必須能恢復並維持虛擬量測應有精度,因此本研究亦提出換模成功的判定條件,經由訂定類神經網路、複迴歸之虛擬量測推估精度以及製程參數整體相似度指標等三項指標之換模門檻值,當此三項指標連續三點降至換模門檻值時即表示換模完成,進而可即時地進行與虛擬量測系統相關之所有服務。以TFT-LCD廠薄膜與黃光製程為例,實驗結果證明該機制能迅速地換模及保持推估準確性,並符合有效達到全廠導入之虛擬量測之目標。
英文摘要 This research devises a virtual metrology automatic model refreshing scheme for fab-wide deployment of the virtual metrology (VM) systems in the semiconductor and TFT-LCD industries. The functionality of the proposed scheme is to create a conjecture model as the base model at the initial stage, and then automatically fan out or port the base model to the same type of equipment that wants conjecture models. Besides, a successful model refreshing process must be able to recover and maintain the conjecture accuracy of the VM systems. Therefore, this research also defines the refresh thresholds for the neural network (NN) index, the multiple regression (MR) index, and the global similarity index (GSI) to differentiate whether a model refreshing process completes. If the values of the three abovementioned indexes are lower than the refresh threshold (RT) for three continuous check points at the same time, it means the model refreshing process is successful, and the VM system is ready to provide services on time. The chemical vapor deposition (CVD) process and the photo process in the TFT-LCD industry are taken as the examples to verify the proposed scheme. Results show that the proposed scheme can perform rapid model refreshing, maintain high conjecture accuracy, and achieve the goal of fab-wide VM system deployment.
論文目次 目 錄
中文摘要
英文摘要
致謝
第一章 緒論
1.1 研究背景1
1.2 研究動機與目的 4
1.3 論文架構5
第二章 文獻探討與理論基礎6
2.1 相關文獻探討6
2.1.1虛擬量測方法6
2.2 相關理論基礎9
2.2.1 簡易循環式類神經網路9
2.2.2 通用回歸類神經網路10
2.2.3 複迴歸11
2.2.4 整體相似度指標12
2.2.5 交互驗證法13
第三章 研究方法14
3.1 自動換模機制說明14
3.1.1 自動換模機制之門檻值14
3.2 實驗案例(一)15
3.2.1實驗描述15
3.2.2實驗條件16
3.2.3實驗項目17
3.2.4實驗過程與結果17
3.3 實驗案例(二)23
3.3.1實驗描述23
3.3.2實驗條件24
3.3.3實驗項目25
3.3.4實驗過程與結果25
3.4 實驗案例(三)29
3.4.1實驗描述29
3.4.2實驗條件31
3.4.3實驗項目32
3.4.4實驗過程與結果32
第四章 結果比較與討論41
4.1 實驗結果分析41
第五章 結論54
5.1結論54
5.2本研究之貢獻54
5.3未來研究方向55
參考文獻56
參考文獻 參考文獻
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[2] Y.-T. Huang, H.-C. Huang, F.-T. Cheng, and T.-S. Liao, and F.-C. Chang, “Automatic Virtual Metrology System Design and Implementation,” in Proc. IEEE International Conference on Automation Science and Engineering, Washington, D.C., U.S.A., August 2008.
[3] P.-H. Chen, S. Wu, J.-S. Lin, F. Ko, H. Lo, J. Wang, C.-H. Yu, and M.-S. Liang, “Virtual Metrology: a Solution for Wafer to Wafer Advanced Process Control,” in Proc. 2005 IEEE International Symposium on Semiconductor Manufacturing, San Jose, CA, Sept. 2005, pp.155-157.
[4] Y.-J. Chang, Y. Kang, C.-L. Hsu, C.-T. Chang, and T. Y. Chan, “Virtual Metrology Technique for Semiconductor Manufacturing,” in Proc. 2006 International Joint Conference on Neural Networks, July 2006, pp. 5289-5293.
[5] J. L. Elman, “Finding Structure in Time,” Cognitive Science, Volume 14, pp. 179-211, 1990.
[6] Donald F. Specht, “A General Regression Neural Network,” IEEE Transactions on Neural Network, Vol. 2, No. 6, pp. 568-576, Nov. 1991.
[7] D. R. Anderson, D. J. Sweeney, T. A. Williams, and J.-C. Chen, Statistics for Business and Economics- A Practical Approach, Singapore: Thomson, 2006.
[8] 曾登琳, “Evaluating Reliance Level of a Virtual Metrology System” ,國立成功大學研究所碩士論文, 2007.
[9] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA: Morgan Kaufman, 2005.
[10] S. Sharma, Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA: Morgan Kaufman, 2005.
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