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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 19, Iss. 1 — Jan. 1, 2002
  • pp: 24–32

Characterization of optical diffraction gratings by use of a neural method

Stéphane Robert, Alain Mure-Ravaud, and Dominique Lacour  »View Author Affiliations


JOSA A, Vol. 19, Issue 1, pp. 24-32 (2002)
http://dx.doi.org/10.1364/JOSAA.19.000024


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Abstract

Optical scatterometry by use of a neural network is now recognized as an efficient method for retrieving dimensions of gratings in semiconductors or glasses. For an on-line control, a small number of measurements and a rapid data treatment are needed. We demonstrate that these requirements can be met by combining data preprocessing and a proper neural learning method. A good accuracy is attainable with the measurement of only a few orders, even in the presence of experimental errors, with a reduction in learning and computing time.

© 2002 Optical Society of America

OCIS Codes
(050.1950) Diffraction and gratings : Diffraction gratings
(120.4630) Instrumentation, measurement, and metrology : Optical inspection
(200.4260) Optics in computing : Neural networks

History
Original Manuscript: January 15, 2001
Revised Manuscript: April 24, 2001
Manuscript Accepted: April 24, 2001
Published: January 1, 2002

Citation
Stéphane Robert, Alain Mure-Ravaud, and Dominique Lacour, "Characterization of optical diffraction gratings by use of a neural method," J. Opt. Soc. Am. A 19, 24-32 (2002)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-19-1-24


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References

  1. A. Roger, D. Maystre, “Inverse scattering method in electromagnetic optics: application to diffraction gratings,” J. Opt. Soc. Am. 70, 1483–1495 (1980). [CrossRef]
  2. K. P. Giapas, R. A. Gottscho, L. A. Clark, J. Kruskal, D. Lambert, A. Kornblit, D. Sinatore, “Use of light scattering in characterizing reactively ion etched profiles,” J. Vac. Sci. Technol. A 9, 664–668 (1991). [CrossRef]
  3. R. Krukar, A. Kornblit, L. A. Clark, J. Kruskal, D. Lambert, E. A. Reitman, R. A. Gottscho, “Reactive ion etching profile and depth characterization using statistical and neural analysis of light scattering data,” J. Appl. Phys. 74, 3698–3706 (1993). [CrossRef]
  4. S. S. H. Naqvi, R. H. Krukar, J. R. McNeil, J. E. Franke, T. M. Niemczyk, D. M. Haaland, R. A. Gottscho, A. Kornblit, “Etch-depth estimation of large-period silicon gratings with multivariate calibration of rigorously simulated diffraction profiles,” J. Opt. Soc. Am. A 11, 2485–2493 (1994). [CrossRef]
  5. R. H. Krukar, S. L. Prins, D. M. Krukar, G. A. Peterson, S. M. Gaspar, J. R. McNeil, S. S. H. Naqvi, “Using scattered light modeling for semiconductor critical dimension metrology and calibration ,” in Integrated Circuit Metrology, Inspection, and Process Control VII, M. T. Postek, ed., Proc. SPIE1926, 60–71 (1993). [CrossRef]
  6. J. Bischoff, J. W. Baumgart, H. Truckenbrodt, J. J. Bauer, “Photoresist metrology based on light scattering,” in Metrology, Inspection, and Process Control for Microlithography X, S. K. Jones, ed., Proc. SPIE2725, 678–689 (1996). [CrossRef]
  7. A. D. Mc Aulay, J. Wang, “Optical diffraction of periodic structures using neural networks,” Opt. Eng. 37, 884–888 (1998). [CrossRef]
  8. J. N. Hwang, C. H. Chan, R. J. Marks, “Frequency selective surface design based on iterative inversion of neural networks,” Presented at International Joint Conference on Neural Networks, Washington, D.C., 1990.
  9. I. Kallioniemi, J. Saarinen, E. Oja, “Optical scatterometry of subwavelength diffraction gratings: neural network approach,” Appl. Opt. 37, 5830–5835 (1998). [CrossRef]
  10. I. Kallioniemi, J. Saarinen, E. Oja, “Characterization of diffraction gratings in a rigorous domain with optical scat-terometry: hierarchical neural-network model,” Appl. Opt. 38, 5920–5930 (1999). [CrossRef]
  11. J. Bischoff, J. Bauer, U. Haak, L. Hutschenreuther, H. Truckenbrodt, “Optical Scatterometry of quarter-micron patterns using neural regression,” in Metrology, Inspection, and Process Control for Microlithography XII, B. Singh, ed., Proc. SPIE3332, 526–537 (1998). [CrossRef]
  12. L. Li, “Multilayer modal method for diffraction gratings of arbitrary profile, depth, and permittivity,” J. Opt. Soc. Am. A 10, 2581–2591 (1993). [CrossRef]
  13. J. Herault, C. Jutten, Réseaux Neuronaux et Traitement du Signal (Editions Hermes, Paris, 1994).
  14. G. Cybenko, “Approximation by superpositions of sigmoidal functions,” Math. Control Signal Syst. 2, 303–314 (1989). [CrossRef]
  15. K. I. Funahashi, “On the approximate realization of continuous mappings by neural networks,” Neural Networks 2, 183–192 (1989). [CrossRef]
  16. D. Nguyen, B. Widrow, “The truck back-upper: an example of self-learning in neural networks,” in Neural Networks for Robotics and Control, W. T. Miller, R. Sutton, P. Werbos, eds. (MIT Press, Cambridge, Mass., 1990), Vol. 12, pp. 287–299.
  17. M. T. Hagan, M. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Trans. Neural Netw. 5, 989–993 (1994). [CrossRef] [PubMed]

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