Artificial neural networks enabled by nanophotonics.

Zhang, Q; Yu, H; Barbiero, M; Wang, B; Gu, M

Artificial neural networks enabled by nanophotonics.

Zhang, Q Yu, H Barbiero, M Wang, B

Gu, M

Permalink

Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: Light Sci Appl, 2019, 8, (1), pp. 42
Issue Date:: 2019

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Download Published versionAdobe PDF (1.99 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, Q
dc.contributor.author	Yu, H
dc.contributor.author	Barbiero, M
dc.contributor.author	Wang, B https://orcid.org/0009-0001-9306-5924
dc.contributor.author	Gu, M
dc.date.accessioned	2025-01-28T00:24:32Z
dc.date.available	2019-03-26
dc.date.available	2025-01-28T00:24:32Z
dc.date.issued	2019
dc.identifier.citation	Light Sci Appl, 2019, 8, (1), pp. 42
dc.identifier.issn	2095-5545
dc.identifier.issn	2047-7538
dc.identifier.uri	http://hdl.handle.net/10453/184259
dc.description.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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	SPRINGERNATURE
dc.relation.ispartof	Light Sci Appl
dc.relation.isbasedon	10.1038/s41377-019-0151-0
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0205 Optical Physics
dc.subject.classification	5102 Atomic, molecular and optical physics
dc.title	Artificial neural networks enabled by nanophotonics.
dc.type	Journal Article
utslib.citation.volume	8
utslib.location.activity	England
utslib.for	0205 Optical Physics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Institute of Biomedical Materials and Devices (IBMD)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-28T00:24:30Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	8
utslib.citation.issue	1

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/184259