Smart Machine Vision for Universal Spatial Mode Reconstruction
Abstract
Structured light beams, in particular those carrying orbital angular momentum (OAM), have gained a lot of attention due to their potential for enlarging the transmission capabilities of communication systems. However, the use of OAM-carrying light in communications faces two major problems, namely distortions introduced during propagation in disordered media, such as the atmosphere or optical fibers, and the large divergence that high-order OAM modes experience. While the use of non-orthogonal modes may offer a way to circumvent the divergence of high-order OAM fields, artificial intelligence (AI) algorithms have shown promise for solving the mode-distortion issue. Unfortunately, current AI-based algorithms make use of large-amount data-handling protocols that generally lead to large processing time and high power consumption. Here we show that a low-power, low-cost image sensor can itself act as an artificial neural network that simultaneously detects and reconstructs distorted OAM-carrying beams. We demonstrate the capabilities of our device by reconstructing (with a 95 efficiency) individual Vortex, Laguerre-Gaussian (LG) and Bessel modes, as well as hybrid (non-orthogonal) coherent superpositions of such modes. Our work provides a potentially useful basis for the development of low-power-consumption, light-based communication devices.
Cite
@article{arxiv.2307.11841,
title = {Smart Machine Vision for Universal Spatial Mode Reconstruction},
author = {José D. Huerta-Morales and Chenglong You and Omar S. Magaña-Loaiza and Shi-Hai Dong and Roberto de J. León-Montiel and Mario A. Quiroz-Juárez},
journal= {arXiv preprint arXiv:2307.11841},
year = {2023}
}