Dynamic recognition and mirage using neuro-metamaterials

被引:93
作者
Qian, Chao [1 ,2 ,3 ]
Wang, Zhedong [1 ,2 ,3 ]
Qian, Haoliang [1 ,2 ,3 ]
Cai, Tong [1 ,2 ,3 ]
Zheng, Bin [1 ,2 ,3 ]
Lin, Xiao [1 ,2 ,3 ]
Shen, Yichen [4 ]
Kaminer, Ido [5 ]
Li, Erping [1 ,2 ,3 ]
Chen, Hongsheng [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Interdisciplinary Ctr Quantum Informat, ZJU UIUC Inst, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ, ZJU Hangzhou Global Sci & Technol Innovat Ctr, Key Lab Adv Micro Nano Elect Devices & Smart Syst, Hangzhou 310027, Peoples R China
[3] Zhejiang Univ, Jinhua Inst, Jinhua 321099, Zhejiang, Peoples R China
[4] Lightelligence Inc, Boston, MA 02210 USA
[5] Technion Israel Inst Technol, Dept Elect & Comp Engn, IL-32000 Haifa, Israel
基金
中国国家自然科学基金;
关键词
D O I
10.1038/s41467-022-30377-6
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
While great progress has been made in object recognition, implementing them is typically based on conventional electronic hardware. Here the authors introduce a concept of neuro-metamaterials that enable a dynamic entirely-optical object recognition and mirage. Breakthroughs in the field of object recognition facilitate ubiquitous applications in the modern world, ranging from security and surveillance equipment to accessibility devices for the visually impaired. Recently-emerged optical computing provides a fundamentally new computing modality to accelerate its solution with photons; however, it still necessitates digital processing for in situ application, inextricably tied to Moore's law. Here, from an entirely optical perspective, we introduce the concept of neuro-metamaterials that can be applied to realize a dynamic object- recognition system. The neuro-metamaterials are fabricated from inhomogeneous metamaterials or transmission metasurfaces, and optimized using, such as topology optimization and deep learning. We demonstrate the concept in experiments where living rabbits play freely in front of the neuro-metamaterials, which enable to perceive in light speed the rabbits' representative postures. Furthermore, we show how this capability enables a new physical mechanism for creating dynamic optical mirages, through which a sequence of rabbit movements is converted into a holographic video of a different animal. Our work provides deep insight into how metamaterials could facilitate a myriad of in situ applications, such as illusive cloaking and speed-of-light information display, processing, and encryption, possibly ushering in an "Optical Internet of Things" era.
引用
收藏
页数:8
相关论文
共 45 条
[1]  
[Anonymous], 2003, Nonlinear optics
[2]   High-Performance Bifunctional Metasurfaces in Transmission and Reflection Geometries [J].
Cai, Tong ;
Tang, ShiWei ;
Wang, GuangMing ;
Xu, HeXiu ;
Sun, ShuLin ;
He, Qiong ;
Zhou, Lei .
ADVANCED OPTICAL MATERIALS, 2017, 5 (02)
[3]   Why future supercomputing requires optics [J].
Caulfield, H. John ;
Dolev, Shlomi .
NATURE PHOTONICS, 2010, 4 (05) :261-263
[4]   Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification [J].
Chang, Julie ;
Sitzmann, Vincent ;
Dun, Xiong ;
Heidrich, Wolfgang ;
Wetzstein, Gordon .
SCIENTIFIC REPORTS, 2018, 8
[5]  
Dutta Sulagna, 2018, Journal of Basic and Clinical Physiology and Pharmacology, V29, P427, DOI 10.1515/jbcpp-2018-0002
[6]   Inverse-designed metastructures that solve equations [J].
Estakhri, Nasim Mohammadi ;
Edwards, Brian ;
Engheta, Nader .
SCIENCE, 2019, 363 (6433) :1333-+
[7]   Laguerre-Gaussian mode sorter [J].
Fontaine, Nicolas K. ;
Ryf, Roland ;
Chen, Haoshuo ;
Neilson, David T. ;
Kim, Kwangwoong ;
Carpenter, Joel .
NATURE COMMUNICATIONS, 2019, 10 (1)
[8]  
Goodman J. W., 1968, INTRO FOURIER OPTICS
[9]   Optical storage arrays: a perspective for future big data storage [J].
Gu, Min ;
Li, Xiangping ;
Cao, Yaoyu .
LIGHT-SCIENCE & APPLICATIONS, 2014, 3 :e177-e177
[10]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778