Deep-learning-based phase control method for tiled aperture coherent beam combining systems

Abstract

We incorporate deep learning (DL) into tiled aperture coherent beam combining (CBC) systems for the first time, to the best of our knowledge. By using a well-trained convolutional neural network DL model, which has been constructed at a non-focal-plane to avoid the data collision problem, the relative phase of each beamlet could be accurately estimated, and then the phase error in the CBC system could be compensated directly by a servo phase control system. The feasibility and extensibility of the phase control method have been demonstrated by simulating the coherent combining of different hexagonal arrays. This DL-based phase control method offers a new way of eliminating dynamic phase noise in tiled aperture CBC systems, and it could provide a valuable reference on alleviating the long-standing problem that the phase control bandwidth decreases as the number of array elements increases.

Fiber laser development enabled by machine learning: review and prospect

“…In 2019, Tianyue Hou et al incorporated a CNN based on supervised learning into tiled aperture CBC systems to learn the relationship between the intensity profile of the combined beam and the relative phases of array elements for the first time [157]. In this way, the required phase for compensation can be obtained directly, which is quite different from the methods based on reinforcement learning [77,78,154,155].…”

Coherent beam combining as a multidisciplinary education and training environment

“…With the rapid development of machine learning and wide application, it is straightforward to rise the proposal that whether machine learning can be employed for the phase control. By encouraging the graduate students to study the related knowledge and test the application potential in phase control, it is found that phase control can be achieved based on convolutional neural network (CNN) can work effectively, which provide a new solution that can generate the phase control signal rapidly without loss of precision [7,8]. Graduate students can also be well trained to master the fundamentals of machine learning.…”

Single step phase optimisation for coherent beam combination using deep learning

“…Wang et al 29 showed that a diffractive optical element can produce a set of interference patterns that can enable phase identification, but that due to the cyclical nature of the interference fringes, the neural network struggled to operate over the full range of phase values (although the authors did show that the approach always converged, and that small phase errors could be solved in a single step). Hou et al 30 discussed again the non-uniqueness between the focal intensity profile and the phase profile with regard to coherent beam combination and proposed using a neural network for phase identification using a camera positioned away from the focal plane. This neural network solution was then improved further by stochastic gradient descent using a camera at the focal plane.…”