Efficient phase locking in massive laser arrays with deep learning from structured data

Abstract Coherent beam combining (CBC) of laser arrays is increasingly attracting attention for generating free-space structured light, unlocking greater potential in aspects such as power scaling, editing flexibility and high-quality light field creation. However, achieving stable phase locking in a CBC system with massive laser channels still remains a great challenge, especially in the presence of heavy phase noise. Here, we propose an efficient phase-locking method for a laser array with more than 1000 channels by leveraging a deep convolutional neural network for the first time. The key insight is that, by elegantly designing the generation strategy of training samples, the learning burden can be dramatically relieved from the structured data, which enables accurate prediction of the phase distribution. We demonstrate our method in a simulated tiled aperture CBC system with dynamic phase noise and extend it to simultaneously generate orbital angular momentum (OAM) beams with a substantial number of OAM modes.