全自動虛擬量測(AVM，Automatic Virtual Metrology)具備了自動化資料擷取、自動化資料品質評估、自動化模型散出與模型更新等能力，可促進虛擬量測的全廠部署，已被應用於一些高科技產業之生產品質監控和機台錯誤偵測。然而，現行AVM系統都是以實體伺服器架設，所以在全廠導入時面臨到許多限制：例如，佔用廠區空間、須由人工設定虛擬量測伺服器、無法同時處理多個虛擬量測要求、無法依照需求自動增加或減少伺服器數量、伺服器利用率不高等。本論文提出一套基於私有雲建制AVM系統的方法論，包括一個整體架構設計，以及自動部署(Automatic Deployment)、自動調節資源(Automatic Scaling)、自動服務(Automatic Serving)與自動多行程處理(Automatic Multi-Process Processing)等四個核心功能機制。整合測試結果顯示，本論文所建置之AVM雲端系統可有效解決上述現行AVM系統的局限，在線上虛擬量測及離線模型建置上也有比較好的效能。

AVM (Automatic Virtual Metrology) possesses several automatic capabilities, such as automatic data acquisition, automatic data quality evaluation, automatic model fanning out, and automatic model refreshing. It can facilitate factory-wide deployment of VM and has been used in production quality monitoring and equipment fault diagnosis in several high-tech industries, i.e. semiconductor, panel, and solar-cell industries. However, the existing AVM system encounters some limitations in factory-wide deployment due to using physical servers, including occupying shop floor spaces, needing manual setting on VM servers, incapable of simultaneously handling multiple VM requests in a VM server, unable to increase or decrease the number of VM servers on demand, and having low usage rates of servers. This thesis proposes a methodology of building a private cloud-based AVM system, which contains an architecture design and four core functional mechanisms, namely Automatic Deployment, Automatic Scaling, Automatic Serving, and Automatic Multi-Process Processing). Integrated testing results that the developed private cloud- based AVM system can effectively remedy the aforementioned limitations of the existing AVM system. In addition, it can achieve better performance than the existing AVM system in on-line virtual metrology and off-line model creation.

參考文獻
[1] SEMI (Semiconductor Equipment and Material International). http://www.semi.org/
[2] 嚴瑞雄，"台灣工具機產業發展概述" ，中工高雄會刊，第18卷，第4期，pp. 85-88，2011年5月。
[3] F.-T. Cheng, H.-C. Huang, and C.-A. Kao, “Developing an Automatic Virtual Metrology System,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 1, pp. 181-188, 2012.
[4] F.-T. Cheng, J. Y.-C. Chang, H.-C. Huang, C.-A. Kao, Y.-L. Chen, and J.-L. Peng, “Benefit Model of Virtual Metrology and Integrating AVM into MES,” IEEE Transactions on Semiconductor Manufacturing, vol. 24, no. 2, pp. 261-272, May 2011.
[5] M. Koitzsch and A. Honold, "Evaluation of Economic Effects as the Basis for Assessing the Investment into Virtual Metrology," Proceedings of SEMATECH Advanced Equipment Control/Advanced Process Control Symposium XXII, Austin, Texas, USA, pp. 172-194, Nov. 1-3, 2010.
[6] B. Gill, T.F. Edgar, and J. Stuber, " A Novel Approach to Virtual Metrology Using Kalman Filtering," Proceedings of SEMATECH Advanced Equipment Control/Advanced Process Control Symposium XXII, Austin, Texas, USA, pp. 267-291, Nov. 1-3, 2010.
[7] J. Moyne, " SEMI-E133: Process Control System Standard (PCS) Standard: Standard Update: Providing Specifications for Virtual Metrology," Proceedings of SEMATECH Advanced Equipment Control/Advanced Process Control Symposium XXII, Austin, Texas, USA, pp. 422-449, Nov. 1-3, 2010.
[8] S.-I. Imai and M. Kitabata, “Prevention of Copper Interconnection Failure in System on Chip Using Virtual Metrology,” IEEE Transactions on Semiconductor Manufacturing, vol. 22, no. 4, pp. 432-437, Nov. 2009.
[9] O. Rothe, “ISMI Next Generation Factory,” e-Manufacturing Workshop, SEMICON West 2008, USA, July, 2008. Available: http://www.sematech.org/meetings/archives/emanufacturing/8546/01-NGF.pdf.
[10] J. Moyne, “International Technology Roadmap for Semiconductors (ITRS) Perspective on AEC/APC,” ISMI AEC/APC Symposium XXI - North America, 2009, Ann Arbor, Michigan USA, Sep. 2009.
[11] B. Kim and K. Park, “Modeling Plasma Etching Process using a Radial Basis Function Network,” Microelectron. Eng., vol. 77, pp. 150–157, 2005.
[12] D. Han, S.B. Moon, K. Park, B. Kim, K.K. Lee, and N.J. Kim, “Modeling of Plasma Etching Process using Radial Basis Function Network and Genetic Algorithm,” Vacuum, vol. 79, pp. 140–147, 2005.
[13] M.-H Hung, T.-H. Lin, F.-T. Cheng, and R.-C. Lin, “A Novel Virtual Metrology Scheme for Predicting CVD Thickness in Semiconductor Manufacturing,” IEEE/ASME Transactions on Mechatronics, vol. 12, no. 3, pp. 308-316, June 2007.
[14] D. Zeng and C.J. Spanos, “Virtual Metrology Modeling for Plasma Etch Operations,” IEEE Transactions on Semiconductor Manufacturing, vol. 22, no. 4, pp. 419-431, Nov. 2009.
 

[15] Y.-C. Su, M.-H. Hung, F.-T. Cheng, and Y.-T. Chen, “A Processing Quality Prognostics Scheme for Plasma Sputtering in TFT-LCD Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 2, pp. 183-194, May 2006.
[16] T.-H. Pan, B.-Q. Sheng, S.-H. Wong, and S.-S. Jang, “A Virtual Metrology System for Predicting End-of-Line Electrical Properties using a MANCOVA Model with Tools Clustering,” IEEE Transactions on Industrial Informatics, vol. 7, no. 2, pp. 187-195, May 2011.
[17] P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” vol 53, issue 6, National Institute of Standards and Technology, Oct. 2009.
[18] M. D. Dikaiakos, D. Katsaros, P. Mehra, G. Pallis, and A. Vakali, "Cloud Computing-Distributed Internet Computing for IT and Scientific Research," IEEE Internet Computing, vol. 13, issue 5, pp.10-13, Sept.-Oct. 2009.
[19] J. Schonwalder, M. Fouquet, G. Rodosek, and I. Hochstatter, "Future Internet = Content + Services + Management," IEEE Communications Magazine, vol. 47, issue 7, pp. 27-33, July 2009.
[20] Cloud Computing Use Cases Discussion Group, “Cloud Computing Use Cases White Paper,” Ver. 4, July 2, 2010. http：//www.cloudusecases.org/
[21] S. C. Misra and A. Mondal, “Identification of a Company‘s Suitability for the Adoption of Cloud Computing and Modeling its Corresponding Return on Investment,” Mathematical and Computer Modeling, no. 53, pp. 504-521, 2011.
[22] N. A. Sultan, "Reaching for the “Cloud”： How SMEs Can Manage," International Journal of Information Management, vol. 31, pp. 272-278, 2011.
[23] S.-H. Jung, et al., “A Private Cloud System for Web-based High-Performance Multiple Sequence Alignment Services,” Proceedings of 2013 4th International Conference on Intelligent Systems, Modelling and Simulation, Seoul, Korea, pp. 143-147, 2013.
[24] L. Xu, L. Liu, and C. Wu, “Services Enhancing Usage of Large Equipment in a Private Cloud,” Proceedings of the IEEE 2013 Conference on Service Science, pp. 118-122, 2013.
[25] I. Miloševi and V. Šimovi, “Comparison of Private Cloud Infrastructure Implementation Models,” Proceedings of the ITI 2012 Conference on Information Technology Interfaces, Cartat, Croatia, pp. 287-292, June 25-28, 2012.
[26] B. Cai, F. Xu, F. Ye, and W. Zhou, "Research and Application of Migrating Legacy Systems to The Private Cloud Platform with CloudStack," Proceeding of the IEEE International Conference on Automation and Logistics, Zhengzhou, China, pp. 400-404, August 2012.
[27] H. Ghanbari, B. Simmonsa, M. Litoiu, and G. Iszlai, "Feedback-based Optimization of a Private Cloud," Future Generation Computer Systems, vol. 28, pp.104–111, 2012.
[28] A. N. Quttoum, et al., "An Optimal Dynamic Resources Partitioning Auction Model for Virtual Private Networks," Telecommunication Systems, vol. 53, pp. 401-414, 2013.
[29] M.-H. Hung, Y.-C. Lin, T. Q. Huy, H.-C. Yang, and F.-T. Cheng, “Development of a Cloud-Computing-based Equipment Monitoring System for Machine Tool Industry,” Proceedings of the 8th annual IEEE Conference on Automation Science and Engineering (CASE 2012), Seoul, Korea, pp. 958-963, August 20-24, 2012.
 

[30] M.-H. Hung, Y.-C. Lin, H.-C. Huang, M.-H. Hsieh, H.-C. Yang, and F.-T. Cheng, “Development of an Advanced Manufacturing Cloud for Machine Tool Industry based on AVM Technology,” Proceedings of the 2013 IEEE Conference on Automation Science and Engineering (CASE 2013), Madison Wisconsin, USA, pp. 195-200, August 17-21, 2013.