Journal
PHYSICAL REVIEW APPLIED
Volume 15, Issue 1, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevApplied.15.014037
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Funding
- National Natural Science Foundation of China [61805149, 61805087, 12047539]
- Guangdong Natural Science Foundation [2020A1515011392, 2019A1515111153, 2018A03031 3368, 2016A030310065]
- Program of Fundamental Research of Shenzhen Science and Technology Plan [JCYJ20200109144001800, JCYJ20180507182035270, GJHZ20180928160407303]
- Science and Technology Planning Project of Guangdong Province [2016B05050 1005]
- Science and Technology Project of Shenzhen [ZDSYS201707271014468]
- International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology [2DMOST2018003]
- China Postdoctoral Science Foundation [2020M682867]
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By utilizing diffractive deep neural networks and the multiple-light-field-modulation capability, it is possible to achieve all-optical signal processing of vortex beams and successfully complete various communication tasks.
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 (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 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, 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 (DNN)-N-2 as a universal light-field-modulation platform.
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