Classification of features in a scene typically requires conversion of the incoming photonic field into the electronic domain. Recently, an alternative approach has emerged whereby passive structured materials can perform classification tasks by directly using free-space propagation and diffraction of light. In this manuscript, we present a theoretical and computational study of such systems and establish the basic features that govern their performance. We show that system architecture, material structure, and input light field are intertwined and need to be co-designed to maximize classification accuracy. Our simulations show that a single layer metasurface can achieve classification accuracy better than conventional linear classifiers, with an order of magnitude fewer diffractive features than previously reported. For a wavelength λ, single layer metasurfaces of size with aperture density achieve ~96% testing accuracy on the MNIST dataset, for an optimized distance ~ to the output plane. This is enabled by an intrinsic nonlinearity in photodetection, despite the use of linear optical metamaterials. Furthermore, we find that once the system is optimized, the number of diffractive features is the main determinant of classification performance. The slow asymptotic scaling with the number of apertures suggests a reason why such systems may benefit from multiple layer designs. Finally, we show a trade-off between the number of apertures and fabrication noise.
Photonic Neuromorphic Computing enabled by a BIC Metasurface
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Pengcheng Laboratory（鹏城实验室）；Quantum Science Center of Guangdong-Hongkong Macao Greater Bay Area（粤港澳大湾区量子科学中心）；Shanxi University（山西大学）；Harbin Institute of Technology（哈尔滨工业大学）
Plasmonic Metasurfaces for Magnetic Skyrmion Control via the Inverse Faraday Effect
Sorbonne Université, CNRS, Institut des NanoSciences de Paris, INSP（索邦大学，法国国家科学研究中心，巴黎纳米科学研究所）；School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology（大连理工大学光电工程与仪器科学学院）；Instituto de Engenharia de Sistemas E Computadores – Microsistemas e Nanotecnologias (INESC MN)（系统计算机工程研究所-微系统与纳米技术）；Institute of Condensed Matter and Nanosciences, Université Catholique de Louvain（天主教鲁汶大学凝聚态与纳米科学研究所）；School of Electronic and Information Engineering, Ningbo University of Technology（宁波理工学院电子与信息工程学院）；Blackett Laboratory, Department of Physics, Imperial College London（伦敦帝国理工学院物理系布莱克特实验室）；London Centre for Nanotechnology, Imperial College London（伦敦帝国理工学院伦敦纳米技术中心）；Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay（巴黎萨克雷大学CNRS-Thales阿尔伯特·费尔特实验室）
Emergent learning: neuromorphic photonic computing with accelerated training
Italian Institute of Technology（意大利理工学院）；University Sapienza（罗马第一大学）；CINECA（CINECA计算中心）；Zhejiang University（浙江大学）；Institute of Nanotechnology of the National Research Council of Italy, CNR-NANOTEC, Rome Unit（意大利国家研究委员会纳米技术研究所，CNR-NANOTEC罗马分部）
Image recognition based on optical spike processing with exciton-polaritons
Faculty of Physics, University of Warsaw（华沙大学物理学院）；Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw（华沙大学数学与计算建模交叉中心）
