Zebin Huang

TL;DR: In this paper, diffractive deep neural networks (DNNs) have been used for all-optical signal processing of VBs by configuring the phase and amplitude distribution of diffractive screens.

TL;DR: It is anticipated that the CNN methods might provide an effective way for identifying OAM modes with high-accuracy and -speed, which may have great potentials in OAM communication, quantum information processing, and astronomical application, etc.

TL;DR: In this article, a few-layer optical diffractive neural networks (ODNNs) are proposed to perform optical logical operations with the orbital angular momentum (OAM) mode and spatial position of multiple OAM modes.

TL;DR: In this article, a diffractive deep neural network (D2NN) was proposed for OAM mode multiplexing and demultiplexing by designing the D2NN model and simulating light propagation through multiple diffractive screens, which can be automatically adjusted to manipulate the wavefront of light beams.

TL;DR: In this paper, a deep feed-forward neural network (FNN) was proposed and investigated for identifying the hybrid orbital angular momentum (OAM) modes of optical vortex beams (VBs).