IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

Feature Based Decision Tree Model for Fault Detection and Classification of Semiconductor Process

반도체 공정의 이상 탐지와 분류를 위한 특징 기반 의사결정 트리

  • Son, Ji-Hun (Department of Information and Industrial Engineering, Yonsei University) ;
  • Ko, Jong-Myoung (Department of Information and Industrial Engineering, Yonsei University) ;
  • Kim, Chang-Ouk (Department of Information and Industrial Engineering, Yonsei University)
  • 손지훈 (연세대학교 정보산업공학과) ;
  • 고종명 (연세대학교 정보산업공학과) ;
  • 김창욱 (연세대학교 정보산업공학과)
  • Received : 2008.12.10
  • Accepted : 2009.02.25
  • Published : 2009.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.

Keywords

References

  1. Baek, J. G., Kim, C. O., and Kim, S. S. (2002), Online learing of the cause-and-effect knowledge of a manufacturing process, International Journal of Production Research, 40(14), 3275-3290 https://doi.org/10.1080/00207540210146921
  2. Cherry, G. and Qin, S. J. (2006), Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis, IEEE Transactions on Semiconductor Manufacturing, 19(2), 159-172 https://doi.org/10.1109/TSM.2006.873524
  3. Choi, J. Y., Ko, J. M., Kim, C. O., Kang, Y. S., and Lee, S. J. (2007), Process start/end event detection and dynamic time warping algorithms for runby-run process fault detection, In Proceedings of International Symposium on Semiconductor Manufacturing, 1-4 https://doi.org/10.1109/ISSM.2007.4446846
  4. Dunia, R., Qin, S. J., Edgar, T. F., and McAvoy, T. J. (1996), Identification of faulty sensors using principal component analysis, AIChE Journal, 42(10), 2797-2812 https://doi.org/10.1002/aic.690421011
  5. Fayyad, U. M. and Irani, K. B. (1993), Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning, In Proceedings of the 13th International Joint conference on Artificial Intelligence, 1022-1027
  6. Goodlin, B. E., Boning, D. S., Sawin, H. H., and Wise, B. M. (2003), imultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools, Journal of Electrochemical Society, 150(12), 778-784 https://doi.org/10.1149/1.1623772
  7. Guo, H.-F., Spanos, C. J., and Miller, A. J. (1991), Real time statistical process control for plasma etching, In Proceedings of IEEE/SEMI International Semiconductor Manufacturing Science Symposium, 113-118 https://doi.org/10.1109/ISMSS.1991.146278
  8. Guyon, I. and Elisseeff, A. (2003), An introduction to variable and feature selection, Journal of Machine Learning Research, 3, 1157-1182 https://doi.org/10.1162/153244303322753616
  9. Irani, K. B., Cheng, J., Fayyad, U. M., and Qian, Z. (1993), Applying machine learning to semiconductor manufacturing, IEEE Intelligent Systems and Their Applications, 8(1), 41-47 https://doi.org/10.1109/64.193054
  10. Kohavi, R. and John, G. (1997), Wrapper for feature subset selection, Artificial Intelligence, 19(1-2), 273-324
  11. Lada, E. E., Lu, J-C., and Wilson, J. R. (2002), A Wavelet-Based Procedure for Process Fault, IEEE Transactions on Semiconductor Manufacturing, 15(1), 79-90 https://doi.org/10.1109/66.983447
  12. Quinlan, J. R. (1986), Induction of decision trees, Machine Learning, 1(1), 81-106
  13. Salvador, S., Chan, P., Brodie, J. (2004), Learning States and Rules for Time Series Anomaly Detection. In Proceedings of the 17th International Florida Artificial Intelligence Research Symposium