Single step phase optimisation for coherent beam combination using deep learning

被引:25
|
作者
Mills, Ben [1 ]
Grant-Jacob, James A. [1 ]
Praeger, Matthew [1 ]
Eason, Robert W. [1 ]
Nilsson, Johan [1 ]
Zervas, Michalis N. [1 ]
机构
[1] Univ Southampton, Optoelect Res Ctr, Southampton SO17 1BJ, Hants, England
基金
英国工程与自然科学研究理事会;
关键词
FIBER LASERS; SCATTERING; FUTURE; ARRAY;
D O I
10.1038/s41598-022-09172-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of similar to 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.
引用
收藏
页数:12
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