Abstract
Virtual metrology (VM) has been applied to semiconductor manufacturing processes for the quality management of wafers. However, noises included in training datasets degrade the performance of VM, which is a key obstacle to the application of VM in real-world semiconductor manufacturing processes. In this paper, we develop a VM dataset construction method by identifying and removing noises. We define noises by considering both input and output variables and classify noises into fault detection and classification (FDC) noises and metrology noises, which have abnormal FDC variables and normal metrology variables, and normal FDC variables and abnormal metrology variables, respectively. We propose the construction of a VM training dataset including FDC noises and excluding metrology noises. By employing novelty detection methods, the normal/abnormal regions of FDC variables are identified. In experiments conducted on a real-world photolithography (photo) data, VM models trained with the dataset constructed by the proposed method showed the best accuracy and the most robustness.
Similar content being viewed by others
References
Kang P, Lee H, Cho S, Kim D, Park J, Park C, Doh S (2009) A virtual metrology system for semiconductor manufacturing. Expert Syst Appl 36:12554–12561
Kourti T, MacGregor JF (1995) Process analysis, monitoring and diagnosis, using multivatiate projection methods. Chemom Intell Lab Syst 28:3–21
Qin SJ (2003) Statistical process monitoring: basics and beyond. J Chemom 17:480–502
Qin SJ, Cherry G, Good R, Wang J, Harrison CA (2006) Semiconductor manufacturing process control and monitoring: a fab-wide framework. J Process Control 16:179–191
Su AJ, Jeng JC, Huang HP, Yu CC, Hung SY, Chao CK (2007) Control relevant issues in semiconductor manufacturing: overview with some new results. Control Eng Pract 15:1268–1279
Cheng J, Cheng FT (2005) Application developmetn to virtual metrology in semiconductor industry. The 32nd annual conference of IEEE Industrial Electronics Society, USA, pp 124–129
Chang YJ, Kang Y, Hsu CL, Chang CT, Chan TY (2006) Virtual metrology technique for semiconductor manufacturing. International joint conference on neural networks, Vancouver, pp 5289–5293
Chen P, Wu S, Lin J, Ko F, Lo H, Wang J, Yu W, Liang MS (2005) Virtual metrology: a solution for wafer to wafer advanced process control. IEEE international symposium on semiconductor manufacturing, USA, pp 155–157
Besnard J, Toprac A (2006) Wafer-to-wafer virtual metrology applied to run-to-run control. In: Proceedings of the 3rd ISMI symposium on manufacturing effectiveness, USA
Kang P, Kim D, Lee H, Doh S, Cho S (2011) Virtual metrology for run-to-run control in semiconductor manufacturing. Expert Syst Appl 38(3):2508–2522
Kim D, Kang P, Cho S, Lee H, Doh S (2012) Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing. Expert Syst Appl 39(4):4075–4083
Su YC, Lin TH, Cheng FT, Wu WM (2008) Accuracy and real-time considerations for implementing various virtual metrology algorithms. IEEE Trans Semicond Manuf 21(3):426–434
Lin TH, Cheng FT, Wu WM, Kao CA, Ye AJ, Chang FC (2009) NN-based key-variable selection method for enhancing virtual metrology accuracy. IEEE Trans Semicond Manuf 22(1):204–211
Chandola V, Banerjee A, Kumar V (2007) Outlier detection: a survey. Technical Report of University of Minnesota, USA
Rousseeuw PJ, Leroy AM (1987) Robust regression and outlier detection. Wiley, New York
Zeng D, Spanos CJ (2009) Virtual metrology modeling for plasma etch operations. IEEE Trans Semicond Manuf 22(4):419–431
He Z, Deng S, Xu X (2002) Outlier detection integrating semantic knowledge. In: Proceedings of the third international conference on advances in web-age information management, Springer, London, pp 126–131
Mitchell M (1996) An introduction to genetic algorithms, MIT Press, Cambridge
Yang J, Honavar V (1998) Feature Subset Selection Using a Genetic Algorithm. IEEE Intell Syst 13(2):44–49
Tax DMJ (2001) One-class classification. Ph.D. Dissertation, Delft University of Technology, Netherland
Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, New York
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York
Jolliffe IT (1986) Principal component analysis. Springer, New York
Hastie T, Tibshirani R, Friedman J (2002) The element of statistical learning: data mining, inference, and prediction. Springer, New York
Kohonen T (1995) Self-organizing maps. Springer series in information science, vol 30. Springer, Berlin
Tax DMJ, Duin RPW (1999) Support vector domain description. Pattern Recognit Lett 20(11–13):1191–1199
Johnson RA, Wichern DW (1998) Applied multivariate statistical analysis. Prentice Hall, Englewood Cliffs, New Jersey
Schölkopf B, Smola AJ (1998) A tutorial on support vector regression. NeuroCOLT2 technical report NC2-TR-1998-030
Kim D, Cho S (2012) Pattern selection for support vector regression-based response modeling. Expert Syst Appl 39(10):8975–8985
Acknowledgments
This work was supported by the Brain Korea 21 PLUS project in 2013, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030814), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (2011-0021893) and 2013 Seoul National University Brain Fusion Program Research Grant. This work was also supported by the Engineering Research Institute of SNU.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, D., Kang, P., Lee, Sk. et al. Improvement of virtual metrology performance by removing metrology noises in a training dataset. Pattern Anal Applic 18, 173–189 (2015). https://doi.org/10.1007/s10044-013-0363-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-013-0363-5