All-Optical Signal Processing of Vortex Beams with Diffractive Deep Neural Networks
Zebin Huang, Peipei Wang, Junmin Liu, Wenjie Xiong, Yanliang He, Jiangnan Xiao, Huapeng Ye, Ying Li, Shuqing Chen, Dianyuan Fan
Abstract
Vortex beams (VBs), possessing a helical phase front and carrying orbital angular momentum (OAM), have attracted considerable attention in optical communications for their mode orthogonality. A platform for achieving all-optical signal processing of VBs, however, remains elusive due to the limited light-field-manipulation capability. We introduce diffractive deep neural networks (${\mathrm{D}}^{2}$NNs) and their applications to process VBs. Exploiting the multiple-light-field-modulation ability of multilayer diffraction structures and the strong data-processing capability of deep neural networks, we reveal that ${\mathrm{D}}^{2}$NNs can manipulate multiple VBs by configuring the phase and amplitude distribution of diffractive screens. The diffraction efficiency and converted-mode purity are greater than 96%. After being trained, ${\mathrm{D}}^{2}$NNs with functions of hybrid-OAM-mode generation, identification, and conversion are obtained, and three typical types of all-optical signal-processing communication, (OAM-shift keying (OAM-SK), OAM multiplexing and demultiplexing, and OAM-mode switching) are successfully achieved. Our simulation results provide an approach that breaks the limitations of poor functionality and complex design in processing VBs, introducing the ${\mathrm{D}}^{2}$NN as a universal light-field-modulation platform.