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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 48, Iss. 28 — Oct. 1, 2009
  • pp: 5318–5323

Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network

Yann Battie, Stéphane Robert, Issam Gereige, Damien Jamon, and Michel Stchakovsky  »View Author Affiliations


Applied Optics, Vol. 48, Issue 28, pp. 5318-5323 (2009)
http://dx.doi.org/10.1364/AO.48.005318


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Abstract

Ellipsometry is an optical technique that is widely used for determining optical and geometrical properties of optical thin films. These properties are in general extracted from the ellipsometric measurement by solving an inverse problem. Classical methods like the Levenberg–Marquardt algorithm are generally too long, depending on direct calculation and are very sensitive to local minima. In this way, the neural network has proved to be an efficient tool for solving these kinds of problems in a very short time. Indeed, it is rapid and less sensitive to local minima than the classical inversion method. We suggest a complete neural ellipsometric characterization method for determining the index dispersion law and the thickness of a simple SiO 2 or photoresist thin layer on Si, SiO 2 , and BK7 substrates. The influence of the training couples on the artificial neural network performance is also discussed.

© 2009 Optical Society of America

OCIS Codes
(120.2130) Instrumentation, measurement, and metrology : Ellipsometry and polarimetry
(200.4260) Optics in computing : Neural networks

ToC Category:
Instrumentation, Measurement, and Metrology

History
Original Manuscript: May 27, 2009
Revised Manuscript: September 3, 2009
Manuscript Accepted: September 6, 2009
Published: September 21, 2009

Citation
Yann Battie, Stéphane Robert, Issam Gereige, Damien Jamon, and Michel Stchakovsky, "Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network," Appl. Opt. 48, 5318-5323 (2009)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-48-28-5318


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References

  1. R. M. A. Azzam and N. M. Bashara, Ellipsometry and Polarized Light (North-Holland, 1977).
  2. O. Polgár, M. Fried, T. Lohner, and I. Bársony, “Evaluation of ellipsometric measurements using complex strategies,” Thin Solid Films 455-456, 95-100 (2004). [CrossRef]
  3. K. Levenberg, “A method for the solution of certain problems in least squares,” Q. Appl. Math. 2, 164-168 (1944).
  4. S. A. Alterovitz and B. Johs, “Multiple minima in the ellipsometric error function,” Thin Solid Films 313-314, 124-127(1998). [CrossRef]
  5. A. Kudla, “Application of the genetic algorithms in spectroscopic ellipsometry,” Thin Solid Films 455-456, 804-808 (2004). [CrossRef]
  6. Y. Zhaoxian and M. Dang, “Generalized simulated annealing algorithm applied in the ellipsometric inversion problem,” Thin Solid Films 425, 108-112 (2003). [CrossRef]
  7. O. Polgár, P. Petrik, T. Lohner, and M. Fried, “Evaluation strategies for multi-layer, multi-material ellipsometric measurements,” Appl. Surf. Sci. 253, 57-64 (2006). [CrossRef]
  8. C. M. Bishop, Neural Networks for Pattern Recognition (Oxford University, 1996).
  9. S. Robert, A. Mure-Ravaud, and D. Lacour, “Characterization of optical diffraction gratings by use of a neural method,” J. Opt. Soc. Am. A 19, 24-32 (2002). [CrossRef]
  10. I. Gereige, S. Robert, D. Jamon, J. J. Rousseau, and G. Granet, “Rapid control of submicrometer periodic structures by a neural inversion from ellipsometric measurement,” Opt. Commun. 278, 270-273 (2007). [CrossRef]
  11. M. F. Tabet and W. A. McGahan, “Use of artificial neural networks to predict thickness and optical constants of thin films from reflectance data,” Thin Solid Films 370, 122-127 (2000). [CrossRef]
  12. M. Fried and L. Rédei, “Non-destructive optical depth profiling and real-time evaluation of spectroscopic data,” Thin Solid Films 364, 64-74 (2000). [CrossRef]
  13. L. Rédei, M. Fried, I. Bársony, and H. Wallinga, “A modified learning strategy for neural networks to support spectroscopic ellipsometric data evaluation,” Thin Solid Films 313-314, 149-155 (1998). [CrossRef]
  14. F. K. Urban III, D. C. Park, and M. F. Tabet, “Development of artificial neural networks for real time, in-situ ellipsometry data reduction,” Thin Solid Films 220, 247-253 (1992). [CrossRef]
  15. F. K. Urban III, D. Barton, and N. I. Boubani, “Extremely fast ellipsometry solutions using cascaded neural networks alone,” Thin Solid Films 332, 50-55 (1998). [CrossRef]

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