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Computing: Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots (Small 20/2017)
Author(s) -
Wang Changhong,
He Wei,
Tong Yi,
Zhang Yishu,
Huang Kejie,
Song Li,
Zhong Shuai,
Ganeshkumar Rajasekaran,
Zhao Rong
Publication year - 2017
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.201770110
Subject(s) - graphene , materials science , quantum dot , nanotechnology , memristor , artificial neural network , reduction (mathematics) , neuromorphic engineering , quantum , optoelectronics , computer science , electronic engineering , physics , quantum mechanics , artificial intelligence , engineering , mathematics , geometry
In article number 1603435 , by Rong Zhao and co‐workers, graphene quantum dots are introduced into memristive devices as nano oxygen‐reservoirs. As a result, repeatable analog states of memristive devices are attained with nearly 85% reduction in variations, indicating highly controllable synaptic weight changes. The controllable artificial synapses could equip neural networks with accurate and efficient learning capability.