High‐Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry

Artificial synapses can boost neuromorphic computing to overcome the inherent limitations of von Neumann architecture. As a promising memristor candidate, ferroelectric tunnel junctions (FTJ) enable the authors to successfully emulate spike‐timing‐dependent synapses. However, the nonlinear and asymm...

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Bibliographic Details
Published in:Advanced functional materials 2022-08, Vol.32 (35), p.n/a
Main Authors: Yang, Shu‐Ting, Li, Xing‐Yu, Yu, Tong‐Liang, Wang, Jie, Fang, Hong, Nie, Fang, He, Bin, Zhao, Le, Lü, Wei‐Ming, Yan, Shi‐Shen, Nogaret, Alain, Liu, Gang, Zheng, Li‐Mei
Format: Article
Language:English
Subjects:
Artificial neural networks
electronic synapses
Ferroelectric domains
Ferroelectric materials
ferroelectric tunnel junctions
Ferroelectricity
Ion migration
linear and symmetric weight changes
Materials science
Memristors
Neuromorphic computing
spike‐timing‐dependent plasticity
Supervised learning
Synapses
Tunnel junctions
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