本研究團隊所研發出之全自動虛擬量測(Automatic Virtual Metrology, AVM)系統，能夠在導入生產線後將所有生產片進行品質監控以改善製程能力及提升良率，達至全面性的生產環境監控。但是生產機台是個時變且動態的系統，其特性會隨著時間與週遭環境的改變而變化。而目前的AVM系統離線建模與線上更新模型時所採用的重要參數是製程專家基於其專業知識所挑選決定的。另外若使用現有之統計方式進行參數篩選，則會因較不具物理意義，使得監控成本提升，以至於難以使製程專家接受並使用之。所以，為求維持且更進一步地提升虛擬量測的精度，就必須研發一個智慧型參數篩選機制(Intelligent Parameter Screening, IPS Scheme)，使得預測模型之重要參數能考慮到專家經驗所挑選之參數，亦能隨著時間與週遭環境的改變而離線或線上動態重選之。上述機制將為進行AVM系統量產應用時，能使得AVM系統的優點與特性充分發揮出來所必須具備的機制。

In the past, our research team had developed the Automatic Virtual Metrology (AVM) System. This system is able to improve process capabilities and to enhance yield rates while monitoring qualities of all production pieces when implemented real-time online. However, production equipment is a dynamic and ever-changing system. The characteristics of equipment will be varied by environmental factors and throughout time period. As a result, the prediction outcomes of the current AVM system don’t seem to be accurate enough just using parameters indicated by experts. On the other hands, parameters chosen by statistical methods could be unacceptable by field process engineers due to the fact that these parameters might not have direct effects on the process and perhaps will raise monitoring cost. Therefore, in order to cost down and to improve VM accuracy, we develop an Intelligent Parameter Screening (IPS) Scheme to choose the key parameters dynamically while also consider experts’ knowledge. By applying the IPS Scheme to the AVM System, we can have better prediction results that improve performances of the AVM System.

[1] F.-T. Cheng, H.-C. Huang, and C.-A. Kao, “Developing an Automatic Virtual Metrology System,” IEEE Trans. on Automation Science and Engineering, vol. 9, no. 1, pp. 181-188., Jan. 2012.
[2] F.-T. Cheng, H.-C. Huang, and C.-A. Kao, "Dual-Phase Virtual Metrology Scheme," IEEE Trans. on Semiconductor Manufacturing, vol. 20, no. 4, pp. 566-571, Nov. 2007.
[3] A. Weber, “Virtual Metrology and Your Technology Watch List: Ten Things You Should Know about This Emerging Technology,” Future Fab International, issue 22, section 4, pp. 52-54, Jan. 2007.
[4] G. R. Pasha, “Selection of Variables in Multiple Regression using Stepwise Regression,” Journal of Research (Science), vol.13, no. 2, pp. 119-127, Dec. 2002.
[5] J. Fan and J. Lv, “A Selective Overview of Variable Selection in High Dimensional Feature Space,” Statistica Sinica, vol. 20, no. 1, pp. 101-148, Jan. 2010.
[6] L. Xu, W. J. Zhang, “Comparison of Different Methods for Variable Selection,” Analytica Chimica Acta, vol. 446, pp. 477–483, June 2001.
[7] W. H. Lin, F. T. Cheng, W. M. Wu, C. A. Kao, A. J. Ye, and F. C. Chang, “NN-based Key Variable Selection Method for Enhancing Virtual Metrology Accuracy,” IEEE Trans. on Semiconductor Manufacturing, vol. 22, no. 1, pp. 204–211, Feb. 2009.
[8] Y. Saeys, I. Inza, and P. Larrañaga, “A Review of Feature Selection Techniques in Bioinformatics,” Bioinformatics Advance Access, vol. 23, issue 19, Aug. 2007.
[9] I.-S. Oh, J.-S. Lee, and B-R Moon, “Hybrid Genetic Algorithms for Feature Selection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1424-1437, Nov. 2004.
[10] L. Yu and H. Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution,” in Proc. 20th International Conference on Machine Learning, pp. 856-863, 2003.
[11] I.-G. Chong and C.-H. Jun, “Performance of Some Variable Selection Methods When Multicollinearity Is Present,” Chemometrics and Intelligent Laboratory Systems, vol. 78, pp. 103-112, March 2005.
[12] Y.-T. Huang, H.-C. Huang, F.-T. Cheng, T.-S. Liao, and F.-C. Chang, “Automatic Virtual Metrology System Design and Implementation,” in Proc. 4th IEEE Conference on Automation Science and Engineering, USA, Aug. 2008.
[13] W-M Wu, F-T Cheng, and F-W Kong, “Dynamic-Moving-Window Scheme for Virtual-Metrology Model Refreshing”, IEEE Trans. on Semiconductor Manufacturing, vol. 25, no. 2, pp. 238-246, May 2012.
[14] S. Wold, M. Sjöström, and L. Eriksson, “PLS-Regression: A Basic Tool of Chemometrics,” Chemometrics and Intelligent Laboratory Systems, vol. 58, issue 2, pp. 109-130, Oct. 2001.
[15] S. Wold, “Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models,” Technometrics, vol. 20, no. 4, part 1, pp.397-405, Nov. 1978.
[16] H. H. Harman, Modern Factor Analysis, 3rd ed. Chicago, IL: Univ. of Chicago Press, 1976.
[17] R. R. Newton and K. E. Rudestam, Your Statistical Consultant, Thousand Oaks, CA: Sage Publications, Inc., 1999.