Diffractive Deep Neural Network for Optical Orbital Angular Momentum Multiplexing and Demultiplexing

Abstract

Vortex beams (VBs), characterized by helical phase front and orbital angular momentum (OAM), have shown perspective potential in improving communication capacity density for providing an additional multiplexing dimension. Here, we propose a diffractive deep neural network (D2NN) method for OAM mode multiplexing and demultiplexing. By designing the D2NN model and simulating light propagation through multiple diffractive screens, the phase and amplitude values can be automatically adjusted to manipulate the wavefront of light beams. Training the D2NN model with mode coupler and separator functions, we convert VBs into target light fields with the diffraction efficiency exceeds 97%, and the mode purities are over 97%. Constructing an OAM multiplexing link, we successfully multiplex and demultiplex two OAM channels that carry 16-QAM signals in simulation, and the demodulated bit-error-rates are below 1×10-4. It is anticipated that the D2NN can perform flexible modulation of multiple OAM modes, which may open a new avenue for high-capacity OAM communication and all-optical information processing, etc.