Deep learning models for effective refractive indices in silicon nitride waveguides

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.