在半導體與TFT-LCD產業中，產品量測資料反應在製程穩定性與產品的可靠度。一般常見的量測資料失效模式分為非製程參數異常所引起的量測錯誤或如外在之粉塵汙染所造成；另外一種失效模式則為由製程參數所造成之產品品質超規等二種類型。為了有效監控量測資料失效模式，本研究提出藉由量測資料品質評估指標(DQIy)來線上即時評估量測資料的品質。DQIy係採用自適應共振理論2與標準變異數方法，來評估第一類量測資料品質之優劣；此外，本論文亦提出產品缺陷偵測機制，此機制利用分類與迴歸樹建構出製程參數與量測超規之規則，偵測第二類產品品質超規之現象，即時分析該產品是否為超規。

此二種機制能應用於一般預測模型架構。以虛擬量測為例，量測資料品質評估機制可應用於量測資料前處理模組，以便剔除異常量測資料，進而避免干擾預測模型。而產品缺陷偵測機制可輔助虛擬量測系統，用以偵測當製程參數資料皆於規格內之情況時，產品亦可能超規之案例，並可發出警訊以提醒製程工程師進行實際量測產品品質並分析產品超規之原因。

In the semiconductor and TFT-LCD industries, metrology data reflects the process stability and product reliability. Generally speaking, metrology data failure modes are divided into two categories: measurement error arising from non-manufacturing process parameters (e.g. external particle pollution of the process) and metrology data abnormality resulted from manufacturing process parameters. In order to effectively monitor the metrology data failure modes, this work proposes an on-line and real-time metrology data quality evaluation scheme to evaluate the quality of the first category metrology data. The metrology data quality evaluation scheme utilizes a metrology data quality index (DQIy), which adopts the Adaptive Resonance Theory 2 and the standard variation method. In addition, this work also proposes a product fault detection scheme (FDS), which utilizes the classification and regression trees to detect and analyze the second category abnormal metrology data in real time to differentiate whether a product is out of specification or not.

Both the two proposed schemes can be applied to the general prediction model framework. For example, in a virtual metrology system, the DQIy can be applied to the data preprocessing module to avoid interference to the prediction model by excluding abnormal metrology data. The FDS can be used to predict possible out-of-specification products and issue warnings to alert engineers to perform practical measurement at the same time.

[1] 2003 International Technology Roadmap for Semiconductors (ITRS), December 2003. http://public.itrs.net/.Y.-C. Chang and F.-T. Cheng, “Application Development of Virtual Metrology in Semiconductor Industry,” in Pro. of the 31st Annual Conference of the IEEE Industrial Electronics (IECON 2005), Raleigh, North Carolina, USA, pp. 124-129, November 2005.
[2] 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 ,USA, pp.155-157, Sept. 2005.
[3] 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.
[4] Y.-C. Su, F.-T. Cheng, M.-H. Hung, and H.-C. Huang, “Intelligent Prognostics System Design and Implementation,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 2, pp. 195-207, May 2006.
[5] 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 (IJCNN’06), pp. 5289-5293, July 2006.
[6] 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,” to appear in IEEE/ASME Transactions on Mechatronics, vol. 12, no. 3, June 2007.
[7] 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.
[8] G. A. Carpenter & S. Grossberg, “ART 2: Self-Organization of Sable Category Recognition Codes for Analog Input Patterns,” Applied Optics, vol.26, no.12, pp.4919-4930, Dec 1987.
[9] G. A. Carpenter & S. Grossberg, “A Massively Parallel Architecture for A Self-Organizing Neural Pattern Recognition Machine”, Computer Vision Graphics Image Processing, vol.13, Issue 7, pp.37-54, July 1987.
[10] G. Spitzlsperger, C. Schmidt, G. Ernst, H. Strasser, and M. Speil, “Fault Detection for a Via Etch Process Using Adaptive Multivariate Methods,” IEEE Transactions on Semiconductor Manufacturing, vol. 18, no. 4, pp. 528-533, Nov. 2005.
[11] C. J. Spanos, H. F. Guo, A. Miller, and J. Levine-Parrill, “Real-Time Statistical Process Control Using Tool Data,” IEEE Transactions on Semiconductor Manufacturing, vol. 5, no. 4, pp. 308-318, Nov. 1992.
[12] T. W. Anderson, “Asymptotic Theory for Principal Component Analysis,” Ann. Math. Statist., vol. 34, pp. 122–148, 1963.
[13] K. A Chamness, “Method and System of Diagnosing a Processing System Using Adaptive Multivariate Analysis,” Tokyo Electro Limited., 2008, U.S. Patent US7328126.
[14] J. Dattorro, Convex Optimization & Euclidean Distance Geometry, Meboo, 2005.
[15] 陳順宇「迴歸分析」，三版，華泰書局，2000。
[16] 溫志平「Cook’s Distance的影響值診斷績效」，國立成功大學統計學研究所碩士論文，2001。
[17] 蔡智政「應用CART決策樹與資料視覺技術於低良率晶圓成因探討」，元智成功大學工業工程與管理研究所碩士論文，2001。
[18] 簡禎富「建構模糊決策樹及其在有交互作用之半導體資料之資料挖礦以提昇良率之研究」，品質學報，vol. 15, pp.193–210, 2008。