Terahertz spoof plasmonic neural network for diffractive information recognition and processing

Xinxin Gao, Ze Gu, Qian Ma (), Bao Jie Chen, Kam-Man Shum, Wen Yi Cui, Jian Wei You, Tie Jun Cui () and Chi Hou Chan ()
Additional contact information
Xinxin Gao: City University of Hong Kong
Ze Gu: Southeast University
Qian Ma: Southeast University
Bao Jie Chen: City University of Hong Kong
Kam-Man Shum: City University of Hong Kong
Wen Yi Cui: Southeast University
Jian Wei You: Southeast University
Tie Jun Cui: Southeast University
Chi Hou Chan: City University of Hong Kong

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hinder miniaturization and broader application. Here, we propose a terahertz spoof plasmonic neural network on a planar diffractive platform for direct multi-target recognition. Our approach employs a spoof surface plasmon polariton coupler array to construct a diffractive network layer, resulting in a compact, efficient, and easily integrable architecture. We designed three schemes: basis vector classification, multi-user recognition, and MNIST handwritten digit classification. Experimental results reveal that the terahertz spoof plasmonic neural network successfully classifies basis vectors, recognizes multi-user orientation information, and directly processes handwritten digits using a designed input framework comprising a metal grating array, transmitters, and receivers. This work broadens the application of terahertz plasmonic metamaterials, paving the way for terahertz on-chip integration, intelligent communication, and advanced computing systems.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-51210-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51210-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-51210-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().