Diffractive neural networks for mode-sorting with flexible detection regions
Optics
2025-08-28 v1
Abstract
Mode-sorting is a procedure that decomposes a light field into a basis of transverse modes, directing each mode into a separate spatial location, allowing the constituent mode intensities to be measured simultaneously. We demonstrate a mode-sorter based on a diffractive optical neural network and show that it is advantageous to include the output detection regions into the trainable set of parameters of that network. This approach outperforms traditional mode-sorting methods, achieving higher efficiency for the same crosstalk levels.
Cite
@article{arxiv.2508.20058,
title = {Diffractive neural networks for mode-sorting with flexible detection regions},
author = {Kaden Bearne and Alexander Duplinskiy and Matthew J. Filipovich and A. I. Lvovsky},
journal= {arXiv preprint arXiv:2508.20058},
year = {2025}
}