Single Step Phase Optimisation for Coherent Beam Combination using Deep Learning

TL;DR

This paper demonstrates a deep learning approach for single-step phase retrieval in coherent beam combination, enabling rapid and precise control of fibre phases for improved beam shaping in laser systems.

Contribution

It introduces a neural network method that retrieves fibre phases from intensity profiles in a single 10 ms step, outperforming iterative methods and assessing profile feasibility.

Findings

Neural network accurately retrieves fibre phases in simulated setups.

Method operates in approximately 10 milliseconds, suitable for real-time applications.

Deep learning shows resilience against simulated experimental noise.

Abstract

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 ~ 10 milliseconds, 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.