Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop
Maksym Shpakovych, Geoffrey Maulion, Vincent Kermène, Alexandre Boju, Paul Armand, Agnès Desfarges‐Berthelemot, Alain Barthélémy
Abstract
An innovative scheme is proposed for the dynamic phase control of a laser beam array. It is based on a simple neural network included in a phase correction loop that predicts the complex field array from the intensity of the induced scattered pattern through a phase intensity transformer made of a diffuser. 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 λ/30.