Simultaneous sorting of fractional vortex beams based on diffractive optical neural network

【摘要】 Fractional vortex beams(FVBs), endowed with unique and complex optical field distributions, exhibit superior potential compared to integer vortex beams in diverse fields such as optical communications. Consequently, the accurate and high-quality sorting of FVBs is of paramount importance. However, traditional methods struggle to meet the stringent requirements for efficiency, crosstalk, and resolution in FVB sorting. Meanwhile, although diffractive optical neural networks(DONNs) have been successfully applied to integer-order orbital angular momentum(OAM) sorting, their application in the non-integer domain remains unexplored. In this paper, we systematically demonstrate the complete workflow and potential efficacy of DONNs in addressing the specific challenges of FVB sorting for the first time. In simulations, we achieve inter-channel crosstalk below -20 dB. Meanwhile, we elaborate on the precise OAM spectrum measurement capability of DONNs and successfully employ DONNs to experimentally measure the spectral distributions of different OAM states for the first time, achieving a measurement fidelity of approximately 99%. This provides a novel all-optical measurement approach for the quantitative evaluation of OAM spectra. Furthermore, we integrate FVB sorting with wavelength-division multiplexing(WDM) technology, enabling our sorting device to maintain stable performance at two wavelengths(532 nm and 660 nm)with an experimental output crosstalk below-10 dB. We believe that this rapid sorting method, which combines the high-speed operation, high efficiency, and high parallelism of optical neural networks, holds tremendous potential for future high-dimensional optical communications, optical metrology, and so on.更多还原

【Abstract】 Fractional vortex beams(FVBs), endowed with unique and complex optical field distributions, exhibit superior potential compared to integer vortex beams in diverse fields such as optical communications. Consequently, the accurate and high-quality sorting of FVBs is of paramount importance. However, traditional methods struggle to meet the stringent requirements for efficiency, crosstalk, and resolution in FVB sorting. Meanwhile, although diffractive optical neural networks(DONNs) have been successfully applied to integer-order orbital angular momentum(OAM) sorting, their application in the non-integer domain remains unexplored. In this paper, we systematically demonstrate the complete workflow and potential efficacy of DONNs in addressing the specific challenges of FVB sorting for the first time. In simulations, we achieve inter-channel crosstalk below -20 dB. Meanwhile, we elaborate on the precise OAM spectrum measurement capability of DONNs and successfully employ DONNs to experimentally measure the spectral distributions of different OAM states for the first time, achieving a measurement fidelity of approximately 99%. This provides a novel all-optical measurement approach for the quantitative evaluation of OAM spectra. Furthermore, we integrate FVB sorting with wavelength-division multiplexing(WDM) technology, enabling our sorting device to maintain stable performance at two wavelengths(532 nm and 660 nm)with an experimental output crosstalk below-10 dB. We believe that this rapid sorting method, which combines the high-speed operation, high efficiency, and high parallelism of optical neural networks, holds tremendous potential for future high-dimensional optical communications, optical metrology, and so on.更多还原