Alagappan, G., Png, C. E. (2018). Modal classification in optical waveguides using deep learning. Journal of Modern Optics, 66(5), 557–561. doi:10.1080/09500340.2018.1552331
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.
License type:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Funding Info:
This research / project is supported by the A*STAR - A*STAR-NTU-SUTD AI Partnership Grant
Grant Reference no. : RGANS1901
This work is supported by NRF-CRP14-2014-04, “Engineering of a Scalable Photonics
Platform for Quantum Enabled Technologies”.
Description:
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Modern Optics on 06 Dec 2018, available online: http://www.tandfonline.com/10.1080/09500340.2018.1552331