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Symbiotic evolution of photonics and artificial intelligence: a comprehensive review

Advanced Photonics 2025年第2期 8-85页

作者： Fu Feng Dewang Huo Ziyang Zhang Yijie Lou Shengyao Wang Zhijuan Gu Dong-Sheng Liu Xinhui Duan Daqian Wang Xiaowei Liu Ji Qi Shaoliang Yu Qingyang Du Guangyong Chen Cuicui Lu Yu Yu Xifeng Ren Xiaocong Yuan Zhejiang Lab Research Center for Frontier Fundamental Studies Westlake Institute for Optoelectronics Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization Beijing Institute of Technology School of Physics Center for Interdisciplinary Science of Optical Quantum and NEMS Integration Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics University of Science and Technology of China CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum Physics Zhejiang Lab Research Center for Life Sciences Computing

The rapid advancement of artificial intelligence(AI) has significantly impacted photonics, creating a symbiotic relationship that accelerates the development and applications of both fields. From the perspective of AI aiding photonics, deep-learning methods and various intelligent algorithms have been developed for designing complex photonic structures, where traditional design approaches fall short. AI's capability to process and analyze large data sets has enabled the discovery of novel materials, such as for photovoltaics,leading to enhanced light absorption and efficiency. AI is also significantly transforming the field of optical imaging with improved performance. In addition, AI-driven techniques have revolutionized optical communication systems by optimizing signal processing and enhancing the bandwidth and reliability of data transmission. Conversely, the contribution of photonics to AI is equally profound. Photonic technologies offer unparalleled advantages in the development of AI hardware, providing solutions to overcome the bottlenecks of electronic systems. The implementation of photonic neural networks, leveraging the high speed and parallelism of optical computing, demonstrates significant improvements in the processing speed and energy efficiency of AI computations. Furthermore, advancements in optical sensors and imaging technologies not only enrich AI applications with high-quality data but also expand the capabilities of AI in fields such as autonomous vehicles and medical imaging. We provide comprehensive knowledge and a detailed analysis of the current state of the art, addressing both challenges and opportunities at the intersection of AI and photonics. The multifaceted interactions between AI and photonics will be explored, illustrating how AI has become an indispensable tool in the development of photonics and how photonics, in turn,facilitates advancements in AI. Through a collection of case studies and examples, we underscore the potential

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Transfer-Learning-Assisted Inverse Metasurface Design for 30% Data Savings

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Physical Review Applied 2022年第2期18卷 024022-024022页

作者： Zhixiang Fan Chao Qian Yuetian Jia Min Chen Jie Zhang Xingshuo Cui Er-Ping Li Bin Zheng Tong Cai Hongsheng Chen ZJU-UIUC Institute Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou 310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou 310027 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China Air and Missile Defend College Air Force Engineering University Xi’an 710051 China

Deep learning is found to be a powerful data-driven force to transform the way we discover, design, and utilize photonics and metasurfaces. More recently, there has been growing interest in deep-learning-enabled on-demand structural design, as it can ease the limitations of low efficiency, time-consuming, and experience navigation in conventional design. However, training data is a valuable source, especially for high-dimensional scatterers. It is extremely challenging and costly to keep the pace of data collection with the increasing degrees of freedom. Here, we propose a transfer-learning-assisted inverse-metasurface-design method to relieve the data dilemma. A flexible transferrable neural network composed of an encoder-decoder network and a physical assistance network is constructed, the latter of which is attached to solve the nonuniqueness problem. Starting from the 5 × 5 metasurface, we successfully migrate the inverse design to a 20 × 20 metasurface, with a Pearson correlation coefficient that reaches 97%. Compared with direct learning, the data requirement is reduced by over 30%. In the experiment, we validate the concept via wave-front customization. Our work constitutes a green and efficient inverse-design paradigm for fast far-field customization and provides a key advance for the next generation of large-scale intelligent metadevices.

关键词： Metamaterials Metasurfaces Photonics Deep learning

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Motor-driven Mechanical Metasurfaces for Dynamic Wireless Channel Management

Motor-driven Mechanical Metasurfaces for Dynamic Wireless Ch...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Zhixiang Fan Yuetian Jia Chao Qian Hongsheng Chen Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-UIUC Institute Zhejiang University Hangzhou China ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang Zhejiang University Hangzhou China Jinhua Institute of Zhejiang University Zhejiang University Jinhua China

Here, we introduce the concept of motor-driven mechanical metasurfaces to manage wireless channel dynamically. Such metasurfaces relieve the heavy reliance on traditional radio-frequency components. The design and reflection spectra of the mechanical metasurfaces is demonstrated between 13.1 GHz and 13.5 GHz, the reflected phase almost covers 2π, while the reflected amplitude remains high. In experiment, the concept is demonstrated through a proof-of-concept far-field customization.

关键词： Wireless communication Radio frequency Phased arrays Power demand Conferences Metasurfaces Reflection

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Global reconstruction of form-free metasurfaces for customized demand via a learning paradigm

Global reconstruction of form-free metasurfaces for customiz...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Yuetian Jia Zhixiang Fan Chao Qian Hongsheng Chen Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-UIUC Institute Zhejiang University Hangzhou China ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang Zhejiang University Hangzhou China Jinhua Institute of Zhejiang University Zhejiang University Jinhua China

Metasurfaces, as a class of two-dimensional artificially structured materials, have recently attracted extensive interest due to their rich optical properties and practically preferable natures, meriting a plethora of unprecedented applications. In all these metasurfaces-related works, a pivotal prerequisite is to obtain high-performance metastructures with the intended optical properties, which is conventionally solved by tedious and inefficient numerical simulations. To facilitate this, state-of-art works introduced deep learning algorithms to streamline the forward and inverse design of individual micro-structures. However, for complex metasurfaces and devices, it still has remained unexplored, with the substantial challenges from unveiling elusive light-metasurfaces interaction, algorithm modeling, and experiment demonstration. To address these, here we report a global and direct intelligent design method for complex metasurfaces and devices, via a deep learning architecture. Illustrative examples of conformal metasurfaces are presented, showing a high-fidelity inverse design from the far-field in the simulation and experiment.

关键词： Deep learning Radio frequency Systematics Design methodology Microwave devices Metasurfaces Microwave theory and techniques

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Performing optical logic operations by a diffractive neural network

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Light(science & Applications) 2020年第1期9卷 1448-1454页

作者： Chao Qian Xiao Lin Xiaobin Lin Jian Xu Yang Sun Erping Li Baile Zhang Hongsheng Chen Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationCollege of Information Science and Electronic EngineeringZhejiang University310027 HangzhouChina ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab.of Advanced Micro/Nano Electronic Devices&Smart Systems of ZhejiangZhejiang University310027 HangzhouChina Department of Electrical Engineering California Institute of TechnologyPasadenaCAUSA ZJU-UIUC Institute Zhejiang University310027 HangzhouChina Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore 637371Singapore

Optical logic operations lie at the heart of optical computing,and they enable many applications such as ultrahighspeed information ***,the reported optical logic gates rely heavily on the precise control of input light signals,including their phase difference,polarization,and intensity and the size of the incident *** to the complexity and difficulty in these precise controls,the two output optical logic states may suffer from an inherent instability and a low contrast ratio of ***,the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is *** such,it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic *** a goal remains ***,we introduce a simple yet universal design strategy,capable of using plane waves as the incident signal,to perform optical logic operations via a diffractive neural ***,the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers,namely,a compound Huygens’*** is,the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer,which provides the information of output logic ***,after training of the diffractive neural network,all seven basic types of optical logic operations can be realized by the same *** a conceptual illustration,three logic operations(NOT,OR,and AND)are experimentally demonstrated at microwave frequencies.

关键词： surface operations precise

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Topological acoustics

arXiv

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arXiv 2023年

作者： Xue, Haoran Yang, Yihao Zhang, Baile Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang ZJU-UIUC Institute Zhejiang University Hangzhou310027 China Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Topological acoustics is an emerging field that lies at the intersection of condensed matter physics, mechanical structural design and acoustics engineering. It explores the design and construction of novel artificial structures, such as acoustic metamaterials and phononic crystals, to manipulate sound waves robustly, taking advantage of topological protection. Early work on topological acoustics was limited to duplicating topological phases that have been understood in condensed matter systems, but recent advances have shifted the paradigm to exploring novel topological concepts that are difficult to realize in other physical systems, such as various topological semimetal phases, and topological phases associated with Floquet engineering, fragile topology, non-Hermiticity and synthetic dimensions. These developments demonstrate the unique advantages of topological acoustic systems and their role in developing topological physics. In this Review, we survey the fundamental mechanisms, basic designs and practical realizations of topological phases in acoustic systems, and provide an overview of future directions and potential applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Observation of Non-Hermitian Skin Effect in Thermal Diffusion

arXiv

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arXiv 2023年

作者： Liu, Yun-Kai Cao, Pei-Chao Qi, Minghong Huang, Qiang-Kai-Lai Peng, Yu-Gui Li, Ying Zhu, Xue-Feng School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Haining314400 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute Zhejiang University Jinhua321099 China Shaoxing Institute Zhejiang University Shaoxing312000 China

The paradigm shift of the Hermitian systems into the non-Hermitian regime profoundly modifies the inherent topological property, leading to various unprecedented effects such as the non-Hermitian skin effect (NHSE). In the past decade, the NHSE effect has been demonstrated in quantum, optical and acoustic systems. Besides in those non-Hermitian wave systems, the NHSE in diffusive systems has not yet been explicitly demonstrated, despite recent abundant advances in the study of topological thermal diffusion. Here we first design a thermal diffusion lattice based on a modified Su-Schrieffer-Heeger model which enables the observation of diffusive NHSE. In the proposed model, the periodic heat exchange rate among adjacent unit cells and the asymmetric temperature field coupling inside unit cells can be judiciously realized by appropriate configurations of structural parameters of unit cells. The transient concentration feature of temperature field on the boundary regardless of initial excitation conditions can be clearly observed, indicating the occurrence of transient thermal skin effect. Nonetheless, we experimentally demonstrated the NHSE and verified the remarkable robustness against various defects. Our work provides a platform for exploration of non-Hermitian physics in the diffusive systems, which has important applications in efficient heat collection, highly sensitive thermal sensing and others. Copyright © 2023, The Authors. All rights reserved.

关键词： Acoustic fields

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Realizing transmitted metasurface cloak by a tandem neural network

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Photonics Research 2021年第5期9卷 I0062-I0068页

作者： ZHENG ZHEN CHAO QIAN YUETIAN JIA ZHIXIANG FAN RAN HAO TONG CAI BIN ZHENG HONGSHENG CHEN ERPING LI Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationCollege of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices&Smart Systems of ZhejiangZhejiang UniversityHangzhou 310027China International Joint Innovation Center ZJU-UIUC Institute Zhejiang UniversityHaining 314400China College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China

Being invisible at will has been a long-standing dream for centuries, epitomized by numerous legends;humans have never stopped their exploration steps to realize this dream. Recent years have witnessed a breakthrough in this search due to the advent of transformation optics, metamaterials, and metasurfaces. However, the previous metasurface cloaks typically work in a reflection manner that relies on a high-reflection background, thus limiting the applications. Here, we propose an easy yet viable approach to realize the transmitted metasurface cloak, just composed of two planar metasurfaces to hide an object inside, such as a cat. To tackle the hard-to-converge issue caused by the nonuniqueness phenomenon, we deploy a tandem neural network(T-NN) to efficiently streamline the inverse design. Once pretrained, the T-NN can work for a customer-desired electromagnetic response in one single forward computation, saving a great amount of time. Our work opens a new avenue to realize a transparent invisibility cloak, and the tandem-NN can also inspire the inverse design of other metamaterials and photonics.

关键词： inverse uniqueness breakthrough

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Electrically tunable helicity of cholesteric heliconical superstructure [Invited]

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Chinese Optics Letters 2020年第8期18卷 22-27页

作者： Conglong Yuan Wenbin Huang Xiaoqian Wang Dong Shen Zhigang Zheng Department of Physics East China University of Science and TechnologyShanghai 200237China School of Optoelectronic Science and Engineering&Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow UniversitySuzhou 215006China Advanced Materials and Liquid Crystal Institute and Chemical Physics Interdisciplinary Program Kent State UniversityKentOhio 44242USA

The dynamic manipulation of the helicity in a cholesteric helical superstructure could enable precise control over its physical and chemical properties, thus opening numerous possibilities for exploring multifunctional *** cholesteric material satisfies the sufficiently small bending elastic effect, an electrically induced deformation named the cholesteric heliconical superstructure is formed. Through theoretical and numerical analysis, we systematically studied the tunable helicity of the heliconical superstructure, including the evolution of the corresponding oblique angle and pitch length. To further confirm the optical properties, Berreman’s 4 × 4 matrix method was employed to numerically analyze the corresponding structure reflection under the dual stimuli of chirality and electric field.

关键词： cholesteric liquid crystal heliconical superstructure tunable reflection band electro-optical materials

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Observation of Multiple Topological Corner States in Thermal Diffusion

arXiv

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arXiv 2023年

作者： Qi, Minghong Wang, Yanxiang Cao, Pei-Chao Zhu, Xue-Feng Gao, Fei Chen, Hongsheng Li, Ying Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China School of Physics Innovation Institute Huazhong University of Science and Technology Wuhan430074 China

Higher-dimensional topological meta-materials have more flexible than one-dimensional topological materials, which are more convenient to apply and solve practical problems. However, in diffusion systems, higher-dimensional topological states have not been well studied. In this work, we experimentally realized the 2D topological structure based on a kagome lattice of thermal metamaterial. Due to the anti-Hermitian properties of the diffusion Hamiltonian, it has purely imaginary eigenvalues corresponding to the decay rate. By theoretical analysis and directly observing the decay rate of temperature through experiments, we present the various corner states in 2D topological diffusive system. Our work constitutes the first realization of multiple corner states with high decay rates in a pure diffusion system, which provides a new idea for the design of topological protected thermal metamaterial in the future. © 2023, CC BY.

关键词： Metamaterials

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