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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Genetic Control of Learning and Prediction: Application to Modeling of Plasma Etch Process Data

학습과 예측의 유전 제어: 플라즈마 식각공정 데이터 모델링에의 응용

  • Published : 2007.04.01
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Abstract

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

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References

  1. C. D. Himmel and G. S. May, 'Advantages of plasma etch modeling using neural networks over statistical techniques,' IEEE Trans. Semicond. Manufact., vol. 6, no. 2, pp. 103-111, 1993 https://doi.org/10.1109/66.216928
  2. S. Bushman, T. F. Edgar, and I. Trachtenberg, 'Modeling of plasma etch systems using ordinary least squares, recurrent neural network, and projection to latent structure models,' .J. Electrochem. Sco., vol. 144, no. 4, pp. 1379-1389, 1997 https://doi.org/10.1149/1.1837600
  3. B. Kim, S. Kim, and B. T. Lee, 'Modeling SiC surface roughness using neural network and atomic force microscopy,' J. Vac. Sci. Technol., vol. 22, no. 5, pp. 2467-2472, 2004 https://doi.org/10.1116/1.1798851
  4. D. E. Rummelhart and J. L. McClelland, Parallel Distributed Processing, Cambridge, M.l.T. Press, 1986
  5. B. Kim and S. Park, 'An optimal neural network plasma model: a case study,' Chemometr Intell. Lab. Syst., vol. 56, no. 1, pp. 39-50,2001 https://doi.org/10.1016/S0169-7439(01)00107-1
  6. D. E. Goldberg, Genetic Algorithms in Search Optimization & Machine Learning, Addison Wesley, Reading MA (1989)
  7. B. Kim, S. Y. Lee, and B. T. Lee, 'Etching of 4H-SiC in a $NF_3/CH_4$ inductively coupled plasma,' J. Vac. Sci. Technol. B. vol. 21, no. 6, pp. 2455-2460, 2003 https://doi.org/10.1116/1.1629715
  8. D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Singapore, 1991