Broad Bandwidth and Highly Efficient Recognition of Optical Vortex Modes Achieved by the Neural-Network Approach

High accuracy recognition of the orbital angular momentum (OAM) of light based on petal interference patterns is demonstrated using a convolutional neural network (CNN) approach with an improved Alexnet structure. A type of hybrid beam carrying OAM is utilized to provide more controllable degrees of freedom to recognize the OAM of light. The relationship between the training sample resolution (or the number associated with the accuracy) and the training time of the model, is presented. The recognition accuracy is closely related with the quantum number l of OAM, the angular ratio n of the spire phase over the hybrid phase in one modulation period, and the propagation distance z. Our studies show that when l ranges from 1 to 10, and n varies from 0.02 to 0.99, the recognition accuracy rate of OAM is nearly 100%. The minimum interval of n recognized at the OAM modes decreases to 0.01, which shows the super-high bandwidth of the generation and detection of OAM modes. Such results suggest great potential for the next generation of CNN-based OAM optical communication applications.