Evidence map›Paper›PMID 31098012›Full record

ArticleLight, science & applications2019

Artificial neural networks enabled by nanophotonics.

Qiming Zhang, Haoyi Yu, Martina Barbiero, Baokai Wang, Min Gu

Abstract read
In one paragraph

Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Photonic-Enabled Energy-Efficient Transparent Neuromorphic Computing Devices: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
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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

5 authors.

Qiming ZhangLaboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001 Australia.
Haoyi YuLaboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001 Australia.
Martina BarbieroLaboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001 Australia.
Baokai WangLaboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001 Australia.
Min GuLaboratory of Artificial-Intelligence Nanophotonics, School of Science, RMIT University, Melbourne, VIC 3001 Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, which is the study of the behaviour of light and the light-matter interaction at the nanometre scale, has unveiled new phenomena and led to new applications beyond the diffraction limit of light. These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs. In the present review, we summarise the recent progress in nanophotonics for emulating the structural, functional and biological features of ANNs, directly or indirectly.

Indexed as

Integrated opticsLaser material processing

Identifiers

PMID31098012
PMCPMC6504946

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.