Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS
MoS2
beyond-Moore
in-memory computing
nanoelectronics
two-dimensional materials
two-dimensional semiconductors
Journal
ACS nano
ISSN: 1936-086X
Titre abrégé: ACS Nano
Pays: United States
ID NLM: 101313589
Informations de publication
Date de publication:
22 Mar 2022
22 Mar 2022
Historique:
pubmed:
16
2
2022
medline:
16
2
2022
entrez:
15
2
2022
Statut:
ppublish
Résumé
Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electronics requires increased energy efficiency of processors for specialized data-driven applications. Here, we show how an in-memory processor fabricated using a two-dimensional materials platform can potentially outperform its silicon counterparts in both standard and nontraditional Von Neumann architectures for artificial neural networks. We have fabricated a flash memory array with a two-dimensional channel using wafer-scale MoS
Identifiants
pubmed: 35167265
doi: 10.1021/acsnano.1c07065
pmc: PMC8945700
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3684-3694Références
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