Abstract

Smooth, continuous-profile diffractive optical elements and other regular microstructures demand advanced characterization methods. Here the problem of reconstructing the profiles with optical scatterometry is addressed for the first time to our knowledge. We represent the profile of a blazed grating with an analytic Chebyshev series. The coefficients of the series are inferred from the far-field diffraction pattern, when the grating profile is illuminated with coherent light. An advanced neural-network model is used as a nonlinear statistical estimator. The reconstruction error is found to be less than 4% as normalized to the depth of the profile, even with realistic measurement errors included in the simulations.

© 2001 Optical Society of America

Characterization of diffraction gratings in a rigorous domain with optical scatterometry: hierarchical neural-network model

Ilkka Kallioniemi, Jyrki Saarinen, and Erkki Oja
Appl. Opt. 38(28) 5920-5930 (1999)

Experimental identification of a grating profile using neural network classifiers in optical scatterometry

Moustapha Godi Tchéré, Stéphane Robert, Zaki Sabit Fawzi, Bernard Bayard, Damien Jamon, and Cécile Gourgon
Appl. Opt. 60(26) 7929-7936 (2021)

Measurement of photoresist grating profiles based on multiwavelength scatterometry and artificial neural network

Shiming Wei and Lifeng Li
Appl. Opt. 47(13) 2524-2532 (2008)

Almost perfect blazing by photonic crystal rod gratings

Evgeny Popov, Bozhan Bozhkov, and Michel Nevière
Appl. Opt. 40(15) 2417-2422 (2001)

Groove profile modification of blazed gratings by dip coating with hardenable liquids

Karlfrid Osterried, Klaus Frieder Heidemann, and Bruno Nelles
Appl. Opt. 37(34) 8002-8007 (1998)