Comparison of Algorithms for Determining the Thickness of Optical Coatings Online

Isaev, Temur; Кочиков, И. В.; Lukyanenko, D. V.; Tikhonravov, Alexander V.; Yagola, A. G.

doi:10.1134/s0965542519030102

Cited by 3 publications

(1 citation statement)

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“…For the Berreman matrix method [26], special features of a material (isotropic vs. anisotropic) are not of principal significance in solving the direct problem. However, the knowledge about such features may significantly simplify solution of the inverse problem by reducing the number of unknown parameters and by making possible the use of simplified computational schemes [27]. In connection with this, we present a classification of the materials of interest [25].…”

Section: Classification Of Mediamentioning

confidence: 99%

Solving the inverse problem for determining the optical characteristics of materials

Lovetski¹,

Zhukov²,

Paukshto³

et al. 2020

Discrete and Continuous Models

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The paper describes a methodology for determining the optical and physical properties of anisotropic thin film materials. This approach allows in the future designing multilayer thin-film coatings with specified properties. An inverse problem of determining the permittivity tensor and the thickness of a thin film deposited on a glass substrate is formulated. Preliminary information on the belonging of a thin-film coating to a certain class can significantly reduce the computing time and increase the accuracy of determining the permittivity tensor over the entire investigated range of wavelengths and film thickness at the point of reflection and transmission measurement Depending on the goals, it is possible to formulate and, therefore, solve various inverse problems: o determination of the permittivity tensor and specification of the thickness of a thick (up to 1 cm) substrate, often isotropic; o determination of the permittivity tensor of a thin isotropic or anisotropic film deposited on a substrate with known optical properties. The complexity of solving each of the problems is very different and each problem requires its own specific set of measured input data. The ultimate results of solving the inverse problem are verified by comparing the calculated transmission and reflection with those measured for arbitrary angles of incidence and reflection.

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Section: Classification Of Mediamentioning

confidence: 99%