Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop
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.
Domaines
Optique / photonique
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Experimental phase control of a 100 laser beam array with quasi-reinforcement learning of a neural network in an error reduction loop.pdf (1.09 Mo)
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