Alagappan, G., & Png, C. E. (2019). Deep learning models for effective refractive indices in silicon nitride waveguides. Journal of Optics, 21(3), 035801. doi:10.1088/2040-8986/ab00d5
Abstract:
This article displays the method of constructing deep learning models for optical mode solving, with a minimal number of exact numerical solutions to Maxwell's equations. We select a silicon nitride channel waveguide and show how the patterns in the effective refractive indices of the fundamental waveguide modes for both polarizations of light, can be uncovered with only 4–16 learning points for the entire parameter space that can be conveniently accessed using existing photo-lithographical and CMOS fabrication techniques. We also illustrate the effect of various transfer functions and neural network layouts to the overall performance of the deep learning model.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - A*STAR-NTU-SUTD AI Partnership Grant
Grant Reference no. : RGANS1901
Description:
This is the Accepted Manuscript version of an article accepted for publication in Journal of Optics. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2040-8986/ab00d5