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Articles

Modal classification in optical waveguides using deep learning

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Pages 557-561 | Received 17 Aug 2018, Accepted 12 Nov 2018, Published online: 06 Dec 2018
 

ABSTRACT

Single-mode operation is crucial in many on-chip integrated photonic devices, and thus the identification of single-mode geometries is an inevitable design requirement. In this article, we develop deep learning (DL) models for ultra-quick classifications of optical waveguide geometries into single- and multi-modal geometries. The DL model accurately predicts the boundary in the parameter space for the geometry of the waveguide that splits the space into single- and multi-modal regions. Using silicon nitride channel waveguide, and targeting both visible and telecommunication wavelengths, we illustrate how DL models can be developed with a minimal number of exact numerical simulations to Maxwell’s equations.

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