International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Real-time Fault Detection in Semiconductor Manufacturing Process : Research with Jade Solution Company

  • Received : 2017.03.12
  • Accepted : 2017.04.05
  • Published : 2017.05.31
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Abstract

Process control is crucial in many industries, especially in semiconductor manufacturing. In such large-volume multistage manufacturing systems, a product has to go through a very large number of processing steps with reentrant) before being completed. This manufacturing system has many machines of different types for processing a high mix of products. Each process step has specific quality standards and most of them have nonlinear dynamics due to physical and/or chemical reactions. Moreover, many of the processing steps suffer from drift or disturbance. To assure high stability and yield, on-line quality monitoring of the wafers is required. In this paper we develop a real-time fault detection system on semiconductor manufacturing process. Proposed system is superior to other incremental fault detection system and shows similar performance compared to batch way.

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References

  1. L.H. Chiang, E.L. Russell, and R.D. Braatz, "Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis", Chemometrics and intelligent laboratory systems, 50(2):243-252, 2000. https://doi.org/10.1016/S0169-7439(99)00061-1
  2. L.H. Chiang, M.E. Kotanchek, and A.K. Kordon, "Fault diagnosis based on fisher discriminant analysis and support vector machines", Computers & chemical engineering, 28(8):1389-1401, 2004. https://doi.org/10.1016/j.compchemeng.2003.10.002
  3. B. Samanta, "Gear fault detection using artificial neural networks and support vector machines with genetic algorithms", Mechanical Systems and Signal Processing, 18(3):625-644, 2004. https://doi.org/10.1016/S0888-3270(03)00020-7
  4. L. Wang, and J. Yu, "Fault feature selection based on modified binary pso with mutation and its application in chemical process fault diagnosis", In Advances in Natural Computation, pp. 832-840, Springer 2005.
  5. Q.P. He, and J. Wang, "Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes", Semiconductor manufacturing, IEEE transactions on, 20(4):345-354, 2007. https://doi.org/10.1109/TSM.2007.907607
  6. J. Korbicz, J.M. Koscielny, Z. Kowalczuk, and W. Cholewa, "Fault diagnosis: models, artificial intelligence, applications", Springer Science & Business Media, 2012.
  7. S. Lee, W. Park, and S. Jung, "Fault detection of aircraft system with random forest algorithm and similarity measure", The Scientific World Journal, 2014.
  8. V.N. Vapnik, "Statistical learning theory", John Wiley & Sons, New York, 1998.
  9. J. Winkeler, B.S. Manjunath, and S. Chandrasekaran, "Subset selection for active object recognition," In CVPR, IEEE Computer Society Press, vol. 2, pp. 511-516, June 1999.
  10. J.A.K. Suykens, and J. Vandewalle, "Least squares support vector machine classifiers," Neural Processing Letters, vol. 9, pp. 293-300, 1999. https://doi.org/10.1023/A:1018628609742