Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지

Design of fuzzy logic Run-by-Run controller for rapid thermal precessing system

고속 열처리공정 시스템의 퍼지 Run-by-Run 제어기 설계

  • 이석주 (한국과학기술연구원 지능제어연구센터) ;
  • 우광방 (연세대학교 자동화기술연구소)
  • Published : 2000.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

Keywords

References

  1. J. F. McGregor, 'Interfaces between process control and on-line statistical process control,' Comp. Syst, Technol. Div. Comm., vol. 10, pp. 9-20, 1987
  2. J. S. Hunter, 'The exponentially weighted moving average,' J. Qual. Technol., vol. 18, pp. 203-210
  3. A. Hu, 'An Optimal bayesian process controller for flexible manufacturing processes,' Ph.D. Thesis, Mechanical Eng. Dept., MIT, 1992
  4. E. Sach, A. Hu, and A. Ingolfsson, 'Run by run process control: combining spc and feedback control,' IEEE Trans. on Semiconductor Manu- facturing, vol. 8, no. 1, pp. 26-43, Feb. 1995 https://doi.org/10.1109/66.350755
  5. J. Tan, H. et al., 'Efficient establishment of a fuzzy logic model for process modeling ad control,' IEEE Trans. on Semiconductor Manu- facturing, vol. 8, no. 1, pp. 50-61, Feb. 1995 https://doi.org/10.1109/66.350757
  6. T. Takagi and M. Sugeno, 'Fuzzy identification of system and its applications to modeling and control,' IEEE SMC, vol. 15, no. 1, pp. 116-132, 1985
  7. Goldberg, Genetic Algotithms in Search, Optimization and machine Learning, Addison-Wesley, 1989
  8. 이석주, 차상엽, 최순혁, 고택범, 우광방, 'EPD 신호 궤적을 이용한 개별 웨이퍼간 이상검출에 관한 연구,' 제어.자동화.시스템공학 논문지, 제4권, 제4호, 1998
  9. J. E. Jackson, A User's Guide to Principal Components. Wiley series in probabilistics and mathematical statistics, pp. 26-47
  10. R. C. Jaeger, Introduction to Micro Electronic Fabrication(Modular Series on Solid State Devices : V.5), Addison-Wesley, MA, 1993
  11. G. E. P. Box, W. Hunter, and J. Hunter, Statistics for Experimenters, New York: Wiley, 1978