Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop
Abstract
An innovative scheme is proposed for the dynamic control of phase in two-dimensional laser beam array. It is based on a simple neural network that predicts the complex field array from the intensity of the induced scattered pattern through a phase intensity transformer made of a diffuser. Iterated phase corrections are applied on the laser field array by phase modulators via a feedback loop to set the array to prescribed phase values. A crucial feature is the use of a kind of reinforcement learning approach for the neural network training which takes account of the iterated corrections. Experiments on a proof of concept system demonstrated the high performance and scalability of the scheme with an array of up to 100 laser beams and a phase setting at 1/30 of the wavelength.
Keywords
Cite
@article{arxiv.2012.05647,
title = {Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop},
author = {Maksym Shpakovych and Geoffrey Maulion and Vincent Kermene and Alexandre Boju and Paul Armand and Agnès Desfarges-Berthelemot and Alain Barthelemy},
journal= {arXiv preprint arXiv:2012.05647},
year = {2021}
}
Comments
12 pages, 9 figures