  The multiplexing and de-multiplexing of orbital angular momentum (OAM) beams
are critical issues in optical communication. Optical diffractive neural
networks have been introduced to perform classification, generation,
multiplexing and de-multiplexing of OAM beams. However, conventional
diffractive neural networks cannot handle OAM modes with a varying spatial
distribution of polarization directions. Herein, we propose a polarized optical
deep diffractive neural network that is designed based on the concept of
rectangular micro-structure meta-material. Our proposed polarized optical
diffractive neural network is trained to classify, generate, multiplex and
de-multiplex polarized OAM beams.The simulation results show that our network
framework can successfully classify 14 kinds of orthogonally polarized vortex
beams and de-multiplex the hybrid OAM beams into Gauss beams at two, three and
four spatial positions respectively. 6 polarized OAM beams with identical total
intensity and 8 cylinder vector beams with different topology charges also have
been classified effectively. Additionally, results reveal that the network can
generate hybrid OAM beams with high quality and multiplex two polarized linear
beams into 8 kinds of cylinder vector beams.
