Inverse design of waveguide grating mode converters using artificial neural networks

TL;DR

This paper demonstrates how deep neural networks can be used to inversely design waveguide grating mode converters by mapping physical features to scattering parameters and optimizing designs via gradient descent.

Contribution

It introduces a neural network-based inverse design method for cascaded-mode converting waveguide gratings, enabling efficient and accurate design based on desired scattering parameters.

Findings

Neural networks effectively map grating features to scattering parameters.

Gradient descent optimization improves the inverse design accuracy.

The method facilitates rapid design of complex waveguide gratings.

Abstract

Machine learning techniques, notably various deep neural network methods, are instrumental in processing extensive and intricate data sets in engineering and scientific fields. This paper shows how deep neural networks can inversely design cascaded-mode converting systems, particularly the waveguide gratings that implement selective mode conversion upon reflection. Neural networks can map the grating's physical features to scattering parameters of the modes reflected from the grating. The trained networks can then be utilized to inversely design the gratings based on the desired values of the scattering parameters. The process of the inverse design involves using the technique of gradient descent of a defined loss function. Minimizing this loss function leads to calculating more accurate features fulfilling the desired scattering parameters.