Premium
Adaptive Crystallite Kinetics in Homogenous Bilayer Oxide Memristor for Emulating Diverse Synaptic Plasticity
Author(s) -
Yin Jun,
Zeng Fei,
Wan Qin,
Li Fan,
Sun Yiming,
Hu Yuandong,
Liu Jialu,
Li Guoqi,
Pan Feng
Publication year - 2018
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201706927
Subject(s) - neuromorphic engineering , memristor , materials science , bilayer , crystallite , nanotechnology , biological system , computer science , electronic engineering , artificial neural network , artificial intelligence , chemistry , biochemistry , membrane , engineering , metallurgy , biology
A critical routine for memristors applied to neuromorphic computing is to approximate synaptic dynamic behaviors as closely as possible. A type of homogenous bilayer memristor with a structure of W/HfO y /HfO x /Pt is designed and constructed in this paper. The memristor replicates the structure and oxygen vacancy ( V O ) distribution of a complete synapse and its Ca 2+ distribution, respectively, after the forming process. The detailed characterizations of its atomic structure and phase transformation in and near the conductive channel demonstrate that the crystallite kinetics are adaptively coupled with the V O migration prompted by directional external bias. The extrusion (injection) of the V O s and the subsequent crystallite coalescence (separation), phase transformation, and alignment (misalignment) resemble closely the Ca 2+ flux and neurotransmitter dynamics in chemical synapses. Such adaptation and similarity allow the memristor to emulate diverse synaptic plasticity. This study supplies a kinetic process of conductive channel theory for bilayer memristors. In addition, our memristor has very low energy consumption (5–7.5 fJ per switching for a 0.5 µm diameter device, compatible with a synaptic event) and is therefore suitable for large‐scale integration used in neuromorphic networks.