Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding
作者机构：Institute of Photonic ChipsUniversity of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence NanophotonicsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China
出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)
年 卷 期：2024年第13卷第3期
页 面：466-477页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the support from the National Natural Science Foundation of China(62005164,62005166) the Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23SG41) the Young Elite Scientist Sponsorship Program by Cast(No.20220042) the Shanghai Natural Science Foundation(23ZR1443700) the Shanghai Rising-Star Program(20QA1404100) the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500) the Shanghai Municipal Science and Technology Major Project,the Shanghai Frontiers Science Center Program(2021-2025 No.20) the National Key Research and Development program of China(Grant Nos.2022YFB2874271)
主 题：polarization mode momentum
摘 要：Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.
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