Transactions on Electrical and Electronic Materials

  • 제6권6호
  • /
  • Pages.262-271
  • /
  • 2005
  • /
  • 1229-7607(pISSN)
  • /
  • 2092-7592(eISSN)
한국전기전자재료학회 (The Korean Institute of Electrical and Electronic Material Engineers)
권호 표지
DOI QR코드

DOI QR Code

Incipient Fault Detection of Reactive Ion Etching Process

  • Hong, Sang-Jeen (Department of Electronic Engineering & NBRC, Myongji University) ;
  • Park, Jae-Hyun (Department of Electronic Engineering & NBRC, Myongji University) ;
  • Han, Seung-Soo (Department of Information Engineering & NPTC, Myongji University)
  • 발행 : 2005.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In order to achieve timely and accurate fault detection of plasma etching process, neural network based time series modeling has been applied to reactive ion etching (RIE) using two different in-situ plasma-monitoring sensors called optical emission spectroscopy (OES) and residual gas analyzer (RGA). Four different subsystems of RIE (such as RF power, chamber pressure, and two gas flows) were considered as potential sources of fault, and multiple degrees of faults were tested. OES and RGA data were simultaneously collected while the etching of benzocyclobutene (BCB) in a $SF_6/O_2$ plasma was taking place. To simulate established TSNNs as incipient fault detectors, each TSNN was trained to learn the parameters at t, t+T, ... , and t+4T. This prediction scheme could effectively compensate run-time-delay (RTD) caused by data preprocessing and computation. Satisfying results are presented in this paper, and it turned out that OES is more sensitive to RF power and RGA is to chamber pressure and gas flows. Therefore, the combination of these two sensors is recommended for better fault detection, and they show a potential to the applications of not only incipient fault detection but also incipient real-time diagnosis.

키워드

참고문헌

  1. D. Montgomery, Introduction to statistical quality control, New York, NY: Wiley, 1991
  2. B. Kim and G. May, 'Real-time diagnosis of semiconductor manufacturing equipment using a hybrid neural network expert system', IEEE Trans. Semi. Manufac., Vol. 20, No.1, p. 39, 1997
  3. C. Almgren, 'The role of RF measurements in plasma etching', Semiconductor International, Vol. 19, No.8, p. 99, 1997
  4. D. H. Holkeboer, T. L. Karandy, F. C. Currier, L. C Frees, and R. E. Ellefson, 'Miniature quadrupole residual gas analyzer for process monitoring at milli'Torr pressure', J. Vac. Soc. Technol. A, Vol. 16, No.3, p. 1157, 1998
  5. J. Roland, P. Marcoux, G. Ray, and G. Rnakin, 'Endpoint detection in plasma etching', J. Vac. Sci. Technol. A, Vol. 3, p. 631, 1985
  6. R. Shadmehr, D. Angell, P. B. Chou, G. S. Oehrlein and R. Jaffe, 'Principal component analysis of optical emission spectroscopy and mass spectrometry: Application to reactive ion etch process parameter estimation using neural networks', J. Electrochem. Soc., Vol. 139, No.3, p. 907, 1992
  7. S. Hong, G. May, and D. Park, 'Neural network modeling of reactive ion etch using optical emission spectroscopy data', IEEE Trans. Semi. Manufac., Vol. 16, No.4, p. 1,2003
  8. J. G. Shabushing and P R. Demiko, 'Application of optical emission spectroscopy to semiconductor device fabrication', American Laboratory, p. 60, 1984
  9. S. Hong, D. Park, and G. May, 'Modeling optical emission spectroscopy data generated during reactive ion etching using an autoencoder neural network', Smart Engineering System Design: Neural Networks, Fussy Logic, Evolutionary Programming, Data Mining and Complex Systems, Vol. 12, p. 945, (C.H. Dagli, Ed.), New York: ASME Press, 2002
  10. V. Cornello, 'RGAs Provides Real Time Process Control', Semi. International, p. 70, 1990
  11. PPT for Wondows User Guide, The UTI Division of MKS Instruments. Inc., 1996
  12. B. Roger, M. Berry, I. Turlik, P. Garrow, and D. Castillo, 'Soft mask for via patterning in benzocyclobutene', Int. J. Micro. and Elect. Packaging, Vol. 17, No.3, p. 210, 3rd Quarter, 1994
  13. I. T. Jolleffe, Principal component analysis, New York, NY: Springer-Verlag, 1986
  14. D. White, D. Boning, S. Butler, and G. Barna, 'Spatial characterization of wafer state using principal component analysis of optical emissions spectra in plasma etch', IEEE Trans. Semi. Manufac., Vol. 10, No.1, p. 52, 1997
  15. C. Himmel and G. May, 'Advantages of plasma etch modeling using neural networks over statistical techniques', IEEE Trans. Semi. Manufac., Vol. 6, No.2, p. 103, 1993
  16. G. May, 'Computational Intelligence in Microelectronics Manufacturing', Chapter 13, The Handbook of Computational Intelligence in Design and Manufacturing (J. Wang and A. Kusiak, Eds.), Boca Raton, FL: CRC Press, 2001
  17. S. Haykin. Neurnl Networks. New York., NY: Macmillan College Puhlishing Company. 1994
  18. S. Hong and G. May. 'Newrai netwot:k hased time series modeling of optical emission spectrosropy data for fault detection in reactive ion etclring'. Proc. of SPIE Conf. on Am:. Microelec. Mamrfac .Santa Clara. CA. Vol. 5041.p. 1.2003

피인용 문헌

  1. Delamination Analysis of Low-Temperature Processed SU-8 Photoresist for MEMS Device Fabrication vol.345-346, pp.1662-9795, 2007, https://doi.org/10.4028/www.scientific.net/KEM.345-346.1397