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Bilayered Oxide‐Based Cognitive Memristor with Brain‐Inspired Learning Activities
Author(s) -
Xiong Wen,
Zhu Li Qiang,
Ye Cong,
Yu Fei,
Ren Zheng Yu,
Ge Zi Yi
Publication year - 2019
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201900439
Subject(s) - neuromorphic engineering , memristor , materials science , von neumann architecture , bottleneck , resistive random access memory , cognition , conductance , computer science , neuroscience , nanotechnology , electronic engineering , computer architecture , optoelectronics , voltage , artificial intelligence , artificial neural network , electrical engineering , psychology , physics , engineering , embedded system , condensed matter physics , operating system
Recently, neuromorphic devices have attracted great attention due to their potential to overcome the von Neumann bottleneck. Due to their nonlinear electrical characteristics and nonvolatile resistance, memristors have been proposed for use in neuromorphic device applications. Bilayered HfO 2 /TiO x ‐based cognitive memristors are proposed. They demonstrate conductance‐modulation capabilities at low operation voltage. Moreover, they can be used to mimic the learning behaviors of biological synapses. The multistore memory model of the brain is exhibited. Furthermore, four types of spike‐timing‐dependent plasticity leaning rules are mimicked by modulating the pre‐ and postsynaptic spikes. In addition, pattern learning and memory behaviors are mimicked. These results indicate that the Pt/HfO 2 /TiO x /TiN cognitive memristor has potential for applications in neuromorphic platforms.