Deep Mode Decomposition: Real-time Mode Decomposition of Multimode Fibers Based on Unsupervised Learning
Min Jiang, Yi An, Rongtao Su, Liangjin Huang, Li Jun, Pengfei Ma, Yanxing Ma, Pu Zhou
Abstract
Mode decomposition (MD), detecting the mode content of a laser beam, is an important technique that has wide applications in research and industry areas. This paper demonstrated a novel non-iterative mode decomposition method, called deep mode decomposition (DeepMD), which relies on a deep neural network with unsupervised learning for the first time. A trained network can directly compute the accurate modal coefficients of the eigenmodes propagating in the fibers, including the modal amplitudes and relative phases with only one near-field intensity image. Regardless of the number of modes, the decomposing rate is similar in simulations, around 571.4 Hz, benefiting real-time applications. The quantitative results of both simulation and real-time experiments show the superiority of our method