# Diffractive neural networks for mode-sorting with flexible detection regions

## TL;DR

This paper introduces a diffractive optical neural network for mode-sorting that optimizes detection regions during training, resulting in higher efficiency and lower crosstalk compared to traditional methods.

## Contribution

The novel approach integrates output detection regions into the training of a diffractive neural network for mode-sorting, enhancing performance.

## Key findings

- Achieves higher efficiency than traditional mode-sorting methods.
- Reduces crosstalk levels in mode separation.
- Demonstrates the advantage of trainable detection regions.

## 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.