arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.05647physics.opticseess.SP
收藏

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 Kermene, Alexandre Boju, Paul Armand, Agnès Desfarges-Berthelemot, Alain Barthelemy

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑