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Machine learning modeling of periodical subwavelength tapers coupling efficiency.

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Autor(es):
das Merces, Viviane Oliveira ; Sisnando, Anderson Dourado ; Rodriguez-Esquerre, V. F. ; Zelinski, ME ; Taha, TM ; Howe, J
Número total de Autores: 6
Tipo de documento: Artigo Científico
Fonte: APPLICATIONS OF MACHINE LEARNING 2021; v. 11843, p. 5-pg., 2021-01-01.
Resumo

The present work deals with the implementation of machine learning algorithms for the analysis of the coupling efficiency of tapers for silicon photonics applications operating in the C band. The analyzed tapers are used for coupling a continuous waveguide with a periodical subwavelength waveguide and they are composed by several segments with variable lengths. The training, testing, and validating data sets have been numerically obtained by an efficient frequency domain finite element method which solves the wave equation and determines the spatial distribution of the electromagnetic fields and the coupling efficiency for each taper configuration. An excellent agreement has been observed for the coupling efficiency calculation using the machine learning algorithms when compared with the one obtained by using the finite element method. (AU)

Processo FAPESP: 15/24517-8 - Fotônica para internet de nova geração
Beneficiário:Hugo Enrique Hernández Figueroa
Modalidade de apoio: Auxílio à Pesquisa - Temático