Reconfigurable MoS

Algorithms Artificial Intelligence Computers Molybdenum Neural Networks, Computer
2D materials artificial intelligence hardware accelerator machine learning neuromorphic computing

Journal

Nano letters
ISSN: 1530-6992
Titre abrégé: Nano Lett
Pays: United States
ID NLM: 101088070

Informations de publication

Date de publication:
11 08 2021
Historique:
pubmed: 21 7 2021
medline: 14 8 2021
entrez: 20 7 2021
Statut: ppublish

Résumé

Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Toward this end, we introduce here a memtransistor with gate-tunable dynamic learning behavior. By fabricating memtransistors from monolayer MoS

Identifiants

DOI: 10.1021/acs.nanolett.1c00982 PMID: 34283622
pubmed: 34283622
doi: 10.1021/acs.nanolett.1c00982
doi:

Substances chimiques

Molybdenum 81AH48963U

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

6432-6440

Auteurs

Jiangtan Yuan (J)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Stephanie E Liu (SE)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Ahish Shylendra (A)

Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.

William A Gaviria Rojas (WA)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Silu Guo (S)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Hadallia Bergeron (H)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Shaowei Li (S)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.

Hong-Sub Lee (HS)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
ORCID: 0000-0001-9605-8984

Shamma Nasrin (S)

Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.

Vinod K Sangwan (VK)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
ORCID: 0000-0002-5623-5285

Amit Ranjan Trivedi (AR)

Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.

Mark C Hersam (MC)

Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States.
Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois 60208, United States.
ORCID: 0000-0003-4120-1426

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Reconfigurable MoS
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Reconfigurable MoS
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Systèmes informatiques › Ordinateurs › Reconfigurable MoS
questionsmedicales.fr › Produits chimiques inorganiques › Métaux › Métaux lourds › Molybdène › Reconfigurable MoS
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Reconfigurable MoS