Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Research Briefing
  • Published:

Using surface plasmons to create programmable neural networks

A spoof surface plasmonic neural network with programmable weights and activation functions was proposed, which has the potential to achieve processing speeds close to the speed of light. This neural network was used to create a wireless communications system that can detect and process electromagnetic waves.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of a programmable cell and its activation function.

References

  1. Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102–114 (2021). A review article that presents various photonic systems for AI and neuromorphic computing applications.

    Article  Google Scholar 

  2. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–647 (2020). A paper that presents an AI chip based on a fully hardware-implemented memristor convolutional neural network.

    Article  Google Scholar 

  3. Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367–373 (2021). This paper reports a reconfigurable diffractive processing unit that can perform large-scale neuromorphic optoelectronic computing.

    Article  MathSciNet  Google Scholar 

  4. Liu, C. et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 2, 113–122 (2022). This study uses a digital-coding metasurface array to develop a reprogrammable diffractive neural network.

    Article  Google Scholar 

  5. Joy, S. R., Erementchouk, M., Yu, H. & Mazumder, P. Spoof plasmon interconnects—communications beyond RC limit. IEEE Trans. Commun. 67, 599–610 (2019). This paper provides information on spoof plasmon interconnects that could lead to fast and reliable data transfer processes in the next generation of communications systems.

    Article  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Gao, X. et al. Programmable surface plasmonic neural networks for microwave detection and processing. Nat. Electron. https://doi.org/10.1038/s41928-023-00951-x (2023).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Using surface plasmons to create programmable neural networks. Nat Electron 6, 266–267 (2023). https://doi.org/10.1038/s41928-023-00952-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-023-00952-w

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing