Deep-learning-assisted, two-stage phase control method for high-power mode-programmable orbital angular momentum beam generation

Abstract

High-power mode-programmable orbital angular momentum (OAM) beams have received substantial attention in recent years. They are widely used in optical communication, nonlinear frequency conversion, and laser processing. To overcome the power limitation of a single beam, coherent beam combining (CBC) of laser arrays is used. However, in specific CBC systems used to generate structured light with a complex wavefront, eliminating phase noise and realizing flexible phase modulation proved to be difficult challenges. In this paper, we propose and demonstrate a two-stage phase control method that can generate OAM beams with different topological charges from a CBC system. During the phase control process, the phase errors are preliminarily compensated by a deep-learning (DL) network, and further eliminated by an optimization algorithm. Moreover, by modulating the expected relative phase vector and cost function, all-electronic flexible programmable switching of the OAM mode is realized. Results indicate that the proposed method combines the characteristics of DL for undesired convergent phase avoidance and the advantages of the optimization algorithm for accuracy improvement, thereby ensuring the high mode purity of the generated OAM beams. This work could provide a valuable reference for future implementation of high-power, fast switchable structured light generation and manipulation.