Optical diffractive deep neural network-based orbital angular momentum mode add–drop multiplexer
Wenjie Xiong, Zebin Huang, Peipei Wang, Xinrou Wang, Yanliang He, Chaofeng Wang, Junmin Liu, Huapeng Ye, Dianyuan Fan, Shuqing Chen
Abstract
Vortex beams have application potential in multiplexing communication because of their orthogonal orbital angular momentum (OAM) modes. OAM add–drop multiplexing remains a challenge owing to the lack of mode selective coupling and separation technologies. We proposed an OAM add–drop multiplexer (OADM) using an optical diffractive deep neural network (ODNN). By exploiting the effective data-fitting capability of deep neural networks and the complex light-field manipulation ability of multilayer diffraction screens, we constructed a five-layer ODNN to manipulate the spatial location of vortex beams, which can selectively couple and separate OAM modes. Both the diffraction efficiency and mode purity exceeded 95% in simulations and four OAM channels carrying 16-quadrature-amplitude-modulation signals were successfully downloaded and uploaded with optical signal-to-noise ratio penalties of ∼1 dB at a bit error rate of 3.8 × 10 −3 . This method can break through the constraints of conventional OADM, such as single function and poor flexibility, which may create new opportunities for OAM multiplexing and all-optical interconnection.