Real‐Time On‐Demand Design of Circuit‐Analog Plasmonic Stack Metamaterials by Divide‐and‐Conquer Deep Learning-Reference-Cited by-同舟云学术

Real‐Time On‐Demand Design of Circuit‐Analog Plasmonic Stack Metamaterials by Divide‐and‐Conquer Deep Learning

Published:2022-12-16 Issue:3 Volume:17 Page:2100738
ISSN:1863-8880
Container-title:Laser & Photonics Reviews
language:en
Short-container-title:Laser & Photonics Reviews

Author:

Xiong Jiankai¹,Shen Jiaqing¹,Gao Yuan¹,Chen Yingshi¹,Ou Jun‐Yu²,Liu Qing Huo³,Zhu Jinfeng¹^ORCID

Affiliation:

1. Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen University Xiamen Fujian 361005 China

2. Optoelectronics Research Centre and Centre for Photonic Metamaterials University of Southampton Highfield Southampton SO17 1BJ UK

3. Department of Electrical and Computer Engineering Duke University Durham NC 27708 USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Publisher

Wiley

Subject

Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/lpor.202100738

Reference39 articles.

1. Ultrabroadband Metasurface for Efficient Light Trapping and Localization: A Universal Surface-Enhanced Raman Spectroscopy Substrate for “All” Excitation Wavelengths

2. Hybrid Metal-Dielectric Metasurfaces for Refractive Index Sensing

3. Biomolecular Sensing at the Interface between Chiral Metasurfaces and Hyperbolic Metamaterials

4. Imprinted plasmonic measuring nanocylinders for nanoscale volumes of materials

5. Interaction and hybridization of orthogonal Fabry-Pérot like surface plasmon modes in metal-dielectric grating structures

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