Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adap...Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adaptability,high latency,and excessive power consumption for deployment.Here,we propose an orbital angular momentum(OAM)-mediated optoelectronic neural network(OOENN)that integrates a diffractive optical module for OAM spectrum feature extraction with a shallow electronic module for turbulence diagnostics,leveraging OAM spectrum data transformation.The optical module extracts turbulence-encoded features from distorted Laguerre–Gaussian(LG)beams and decomposes its output field into OAM spectrum data.These data are then fed into an electronic module that diagnoses turbulence strength using a minimal fully connected network with 9 input neurons and nonlinear activation.The OOENN performs feature extraction at light speed while enabling ultra-efficient electronic processing,thereby alleviating the latency and power constraints.Experimental results demonstrate diagnostics of five turbulence strengths within C_(n)^(2)=10-16 to 10-12 m^(-2∕3),achieving 82.4%accuracy at 80 ms latency per diagnosis.This fusion of structured light fields with optoelectronic intelligence establishes a technological foundation for next-generation adaptive systems in turbulence-resilient optical communications,remote sensing,and quantum information transfer.展开更多
基金National Natural Science Foundation of China(62422509,62575180)Natural Science Foundation of Shanghai Municipality(23ZR1443700,25ZR1402386)+3 种基金Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23SG41)Young Elite Scientist Sponsorship Program by Cast(20220042)Shanghai Municipal Science and Technology Major ProjectShanghai Frontiers Science Center Program(2021±2025 No.20)。
文摘Atmospheric turbulence(AT)severely degrades free-space communications,imaging,and sensing systems,driving critical demand for diagnostics of turbulence strength(C_(n)^(2)).However,existing approaches face limited adaptability,high latency,and excessive power consumption for deployment.Here,we propose an orbital angular momentum(OAM)-mediated optoelectronic neural network(OOENN)that integrates a diffractive optical module for OAM spectrum feature extraction with a shallow electronic module for turbulence diagnostics,leveraging OAM spectrum data transformation.The optical module extracts turbulence-encoded features from distorted Laguerre–Gaussian(LG)beams and decomposes its output field into OAM spectrum data.These data are then fed into an electronic module that diagnoses turbulence strength using a minimal fully connected network with 9 input neurons and nonlinear activation.The OOENN performs feature extraction at light speed while enabling ultra-efficient electronic processing,thereby alleviating the latency and power constraints.Experimental results demonstrate diagnostics of five turbulence strengths within C_(n)^(2)=10-16 to 10-12 m^(-2∕3),achieving 82.4%accuracy at 80 ms latency per diagnosis.This fusion of structured light fields with optoelectronic intelligence establishes a technological foundation for next-generation adaptive systems in turbulence-resilient optical communications,remote sensing,and quantum information transfer.
