Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop

TL;DR

This paper presents a novel neural network-based control scheme for phase management in a 100-beam laser array, utilizing a quasi-reinforcement learning approach within an error reduction loop, demonstrating high performance and scalability.

Contribution

It introduces a new neural network training method incorporating reinforcement learning for dynamic phase control in laser arrays.

Findings

Successful experimental demonstration with 100 laser beams.

Achieved phase accuracy at 1/30 of the wavelength.

High scalability and performance of the control scheme.

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.