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Pis'ma v Zhurnal Èksperimental'noi i Teoreticheskoi Fiziki, 2023, Volume 118, Issue 7, Pages 513–518
DOI: https://doi.org/10.31857/S1234567823190072
(Mi jetpl7053)
 

CONDENSED MATTER

Convolutional neural networks for predicting morphological and nonlinear optical properties of thin films of quasi-two-dimensional materials

A. A. Popkova, A. A. Fedyanin

Faculty of Physics, Moscow State University, Moscow, 119991 Russia
References:
Abstract: Two-dimensional materials are promising candidates for the creation of flat photonics devices. The main problem of using such materials for applied applications is the complexity of creating films of specified geometric parameters. The films of two-dimensional materials made by exfoliation or chemical deposition methods are usually randomly distributed over a large area and have a large thickness spread. In this paper, we use convolutional neural networks to predict the film thickness of a quasi-two-dimensional material based on optical microscopy data. Hexagonal boron nitride, which is actively used in the creation of flat electronic and optoelectronic devices, was chosen as a test material. Due to the high spatial resolution of microscopy, it is possible to achieve high accuracy in predicting the thicknesses of flat areas of the sample, which allows for rapid characterization of structures. In addition, using the example of the signal of the third optical harmonic, we show the possibility of predicting the magnitude of the nonlinear optical response of the film, which expands the scope of the method.
Funding agency Grant number
Foundation for the Development of Theoretical Physics and Mathematics BASIS 19-2-6-28-1
The work was supported of the Foundation for the Development of Science and Education Intellect and the Foundation for the Advancement of Theoretical Physics and Mathematics BASIS (project no. 19-2-6-28-1).
Received: 10.08.2023
Revised: 24.08.2023
Accepted: 26.08.2023
English version:
Journal of Experimental and Theoretical Physics Letters, 2023, Volume 118, Issue 7, Pages 502–507
DOI: https://doi.org/10.1134/S0021364023602725
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. A. Popkova, A. A. Fedyanin, “Convolutional neural networks for predicting morphological and nonlinear optical properties of thin films of quasi-two-dimensional materials”, Pis'ma v Zh. Èksper. Teoret. Fiz., 118:7 (2023), 513–518; JETP Letters, 118:7 (2023), 502–507
Citation in format AMSBIB
\Bibitem{PopFed23}
\by A.~A.~Popkova, A.~A.~Fedyanin
\paper Convolutional neural networks for predicting morphological and nonlinear optical properties of thin films of quasi-two-dimensional materials
\jour Pis'ma v Zh. \`Eksper. Teoret. Fiz.
\yr 2023
\vol 118
\issue 7
\pages 513--518
\mathnet{http://mi.mathnet.ru/jetpl7053}
\crossref{https://doi.org/10.31857/S1234567823190072}
\edn{https://elibrary.ru/xrdazh}
\transl
\jour JETP Letters
\yr 2023
\vol 118
\issue 7
\pages 502--507
\crossref{https://doi.org/10.1134/S0021364023602725}
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