Deep Mode Decomposition: Real-Time Mode Decomposition of Multimode Fibers Based on Unsupervised Learning

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 weights 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.