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Article Dans Une Revue Optics Express Année : 2021

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

Maksym Shpakovych
  • Fonction : Auteur
Geoffrey Maulion
  • Fonction : Auteur
Vincent Kermène
Alexandre Boju
  • Fonction : Auteur
Paul Armand
  • Fonction : Auteur
Agnes Desfarges-Berthelemot
  • Fonction : Auteur
Alain Barthelemy
  • Fonction : Auteur

Résumé

An innovative scheme is proposed for the dynamic phase control of 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 /30.
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Dates et versions

hal-03426207 , version 1 (12-11-2021)

Identifiants

  • HAL Id : hal-03426207 , version 1

Citer

Maksym Shpakovych, Geoffrey Maulion, Vincent Kermène, Alexandre Boju, Paul Armand, et al.. Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop. Optics Express, 2021, 29 (8), pp.12307-12318. ⟨hal-03426207⟩

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