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Artificial Synapses: Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing (Adv. Mater. 36/2018)
Author(s) -
Sharbati Mohammad Taghi,
Du Yanhao,
Torres Jorge,
Ardolino Nolan D.,
Yun Minhee,
Xiong Feng
Publication year - 2018
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201870273
Subject(s) - neuromorphic engineering , materials science , graphene , scalability , synapse , nanotechnology , artificial neural network , memristor , power (physics) , computer architecture , optoelectronics , computer science , electronic engineering , neuroscience , artificial intelligence , engineering , physics , database , biology , quantum mechanics
By reversibly intercalating ions between the layers of two‐dimensional graphene, Feng Xiong and co‐workers at Pitt develop a novel artificial synapse for neuromorphic computing, as described in article number 1802353 . With over 250 tunable analog states, good energy efficiency, and promising scalability, these electrochemical synapses can lead to the hardware implementation of neural networks and hence the prevalent use of artificial intelligence.