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Designing artificial neural networks for band structures computations in photonic crystals
Author(s): Adriano da S. Ferreira; Gilliard N. Malheiros-Silveira; Hugo E. Hernández-Figueroa
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Paper Abstract

We modeled Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks (ANNs) for computing band structures (BSTs) and photonic band gaps (PBGs) of 2D and 3D photonic crystals (PhCs). We aim at providing fast ANN models which might boost the computations of BDs and PBGs regarding electromagnetic solvers. The case studies considered 2D and 3D PhCs with different lattices, geometries, and materials. Datasets for ANN training were built by varying the geometric shapes' dimensions and the dielectric constants of the case-study PhCs. We demonstrate simple and fast-training ANNs capable of providing accurate BSTs and PGBs through speedy computations.

Paper Details

Date Published: 26 February 2019
PDF: 8 pages
Proc. SPIE 10912, Physics and Simulation of Optoelectronic Devices XXVII, 109121N (26 February 2019); doi: 10.1117/12.2510739
Show Author Affiliations
Adriano da S. Ferreira, Univ. Estadual de Campinas (Brazil)
São Paulo Federal Institute of Education, Science and Technology (Brazil)
Gilliard N. Malheiros-Silveira, Univ. Estadual de Campinas (Brazil)
São Paulo State Univ. (Brazil)
Hugo E. Hernández-Figueroa, Univ. Estadual de Campinas (Brazil)


Published in SPIE Proceedings Vol. 10912:
Physics and Simulation of Optoelectronic Devices XXVII
Bernd Witzigmann; Marek Osiński; Yasuhiko Arakawa, Editor(s)

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