Synaptic transistor based on reversible hydrogenation of graphene channel

Y Yiqian Hu (Department of Physics, Shanghai Normal University , Shanghai 200232,) L Lei Huang (BLSA-ZJU Research Center and Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.) Q Quanhong Chang (Department of Physics, Shanghai Normal University , Guilin Road 100, Shanghai 200234,) X Xun Peng (Department of Physics, Shanghai Normal University 1 , Guilin Road 100, Shanghai 200234,) G Gujin Hu (Department of Physics, Shanghai Normal University , Shanghai 200232,) W Wangzhou Shi (Department of Physics, Shanghai Normal University , Shanghai 200232,)

Abstract

Graphene transistors with a gate-controlled transition of neuromorphic functions between artificial neurons and synapses have attracted increasing attention because the atomic thickness could be easily modulated by different stimuli, which is very beneficial for synaptic applications. As a modulation method, a graphene electrolyte-gated transistor (EGT) has been proposed, in which the electrical conductance of the graphene channel is modulated by reversible electrochemical hydrogenation of graphene. However, only a sparse physically realized graphene-based synaptic H+-EGTs have been reported due to the difficulty of achieving a high concentration of protons at the electrolyte–graphene interface. Here, we have reported the H+-EGTs with a highly defective graphene channel and a gel electrolyte [H3PO4/poly(vinyl alcohol)], which is based on hydrogenation and dehydrogenation of highly defected-graphene, performing the similar functions as the common artificial synaptic transistors, with good retention (<1% attenuation per minute), analog tunability (>200 nonvolatile states), and precisely controllable resistance (∼0.4% step flipped per synaptic event). In addition, the cyclic voltammetry test was applied to confirm the hydrogenation and dehydrogenation of the graphene channel. It is expected that this principle can provide ideas for designing graphene-based artificial synapses enabling integrated functions of in-memory computing and in-memory sensing for the neuromorphic system.

Article Details

Volume / Issue Vol. 126, Issue 1
Published January 06, 2025
ISSN 0003-6951
Publisher American Institute of Physics

Journal Info

Applied Physics Letters

American Institute of Physics

ISSN: 0003-6951 Physical Sciences

Authors (6)

Y

Yiqian Hu

Department of Physics, Shanghai Normal University , Shanghai 200232,

L

Lei Huang

BLSA-ZJU Research Center and Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.

Q

Quanhong Chang

Department of Physics, Shanghai Normal University , Guilin Road 100, Shanghai 200234,

X

Xun Peng

Department of Physics, Shanghai Normal University 1 , Guilin Road 100, Shanghai 200234,

G

Gujin Hu

Department of Physics, Shanghai Normal University , Shanghai 200232,

W

Wangzhou Shi

Department of Physics, Shanghai Normal University , Shanghai 200232,