Evidence map›Paper›PMID 35624107›Full record

ArticleLight, science & applications2022

Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible.

Xuhao Luo, Yueqiang Hu, Xiangnian Ou, Xin Li, Jiajie Lai, Na Liu, Xinbin Cheng, Anlian Pan, Huigao Duan

Abstract read
In one paragraph

Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. XOR-Logic Phase Coding Programmable Metasurface for Low Power-Consumption Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  7. Review
  8. High-clockrate free-space optical in-memory computing.Light, science & applications · 2026
    Article
  9. Article
  10. Compound Meta-Optics for Advanced Optical Engineering.Sensors (Basel, Switzerland) · 2026
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Review
  20. Article

9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xuhao Luo *National Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
Yueqiang Hu *National Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China. huyq@hnu.edu.cn.ORCID http://orcid.org/0000-0002-4395-4299
Xiangnian OuNational Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
Xin LiNational Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
Jiajie LaiNational Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.
Na Liu2nd Physics Institute, University of Stuttgart, Pfaffenwaldring 57, 70569, Stuttgart, Germany.ORCID http://orcid.org/0000-0001-5831-3382
Xinbin ChengInstitute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai, 200092, China.ORCID http://orcid.org/0000-0002-3855-483X
Anlian PanNational Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China.ORCID http://orcid.org/0000-0003-3335-3067
Huigao DuanNational Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China. duanhg@hnu.edu.cn.ORCID http://orcid.org/0000-0001-9144-2864

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52005175
6 · The paper itself

Abstract

Replacing electrons with photons is a compelling route toward high-speed, massively parallel, and low-power artificial intelligence computing. Recently, diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking in the visible. The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25 × 10

Identifiers

PMID35624107
PMCPMC9142536

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.