首页|Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

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Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

optical neural networksdiffractive deep neural networkscascaded metasurfaces

Cong He、Dan Zhao、Fei Fan、Hongqiang Zhou、Xin Li、Yao Li、Junjie Li、Fei Dong、Yin-Xiao Miao、Yongtian Wang、Lingling Huang

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Beijing Engineering Research Center of Mixed Reality and Advanced Display,Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China,School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China

Institute of Modern Optics,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Nankai University,Tianjin 300350,China

Department of Physics and Optoelectronics,Faculty of Science,Beijing University of Technology,Beijing 100124,China

Beijing National Laboratory for Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences,Beijing 100191,China

Beijing Aerospace Institute for Metrology and Measurement Technology,Beijing 100076,China

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National Key R&D Program of ChinaBeijing Outstanding Young Scientist ProgramNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science ParkSynergetic Extreme Condition User Facility(SECUF)

2021YFA1401200BJJWZYJH01201910007022U21A201409205011762005017Z211100004821009

2024

10.29026/oea.2024.230005

光电进展(英文版)

光电进展(英文版)

EI
ISSN：
年,卷(期)：2024.7(2)
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