Polarized deep diffractive neural network for classification, generation, multiplexing and de-multiplexing of orbital angular momentum modes
Jiaqi Zhang, Zhiyuan Ye, Jianhua Yin, Liying Lang, Shuming Jiao

TL;DR
This paper introduces a polarized deep diffractive neural network capable of classifying, generating, multiplexing, and de-multiplexing polarized OAM beams, advancing optical communication technologies with high accuracy and versatility.
Contribution
It presents a novel polarized optical deep diffractive neural network based on micro-structure meta-materials for handling polarized OAM modes, which was not addressed by previous models.
Findings
Successfully classified 14 types of orthogonally polarized vortex beams.
De-multiplexed hybrid OAM beams into multiple Gaussian beams at various positions.
Generated high-quality hybrid OAM beams and multiplexed polarized linear beams into diverse cylinder vector beams.
Abstract
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…
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