Neural Network Enabled Common-Path Phase Locking for Filled-Aperture Coherent Beam Combining

Deep learning (DL) has been applied to phase control in coherent beam combination (CBC) recently.However, existing DL-based approaches for filled-aperture CBC essentially convert the phase-locking path into tiledaperture schemes.Consequently, common-path phase-locking in DL-based filled-aperture CBC remains unrealized.Common-path refers to a phase-locking scheme in which the phase information is extracted from the combined beam after the same combining system.In this paper, a common-path phase-locking method is proposed.By exploiting the intrinsic non-uniformity, each laser source is effectively labeled, enabling a mapping between the combined speckle and the multi-source phase.A neural network is employed to reconstruct the phase.Simulations with 25-channel CBC demonstrate a phase-locking accuracy of up to /39.Notably, it remains effective under dynamic phase disturbances.Our work presents a common-path phase-locking approach based on neural network for filled-aperture CBC, which can offer a new solution for the field.