Diffractive neural networks for mode-sorting with flexible detection regions

Fuente: arXiv
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Main Authors: Bearne, Kaden, Duplinskiy, Alexander, Filipovich, Matthew J., Lvovsky, A. I.
Format: Preprint
Published: 2025
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author Bearne, Kaden
Duplinskiy, Alexander
Filipovich, Matthew J.
Lvovsky, A. I.
author_facet Bearne, Kaden
Duplinskiy, Alexander
Filipovich, Matthew J.
Lvovsky, A. I.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_20058
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Diffractive neural networks for mode-sorting with flexible detection regions
Bearne, Kaden
Duplinskiy, Alexander
Filipovich, Matthew J.
Lvovsky, A. I.
Optics
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.
title Diffractive neural networks for mode-sorting with flexible detection regions
topic Optics
url https://arxiv.org/abs/2508.20058