Inherent stochasticity during insulator–metal transition in VO 2
Author(s) -
Shaobo Cheng,
MinHan Lee,
Richard Tran,
Yin Shi,
Xing Li,
Henry Navarro,
Coline Adda,
Qingping Meng,
LongQing Chen,
R. C. Dynes,
Shyue Ping Ong,
Iván K. Schuller,
Yimei Zhu
Publication year - 2021
Publication title -
proceedings of the national academy of sciences
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2105895118
Subject(s) - neuromorphic engineering , resistive touchscreen , resistive random access memory , metal–insulator transition , transmission electron microscopy , materials science , monoclinic crystal system , nanotechnology , optoelectronics , thermal conduction , condensed matter physics , computer science , physics , metal , chemistry , crystallography , electrode , artificial neural network , artificial intelligence , metallurgy , quantum mechanics , crystal structure , composite material , computer vision
Vanadium dioxide (VO 2 ), which exhibits a near-room-temperature insulator-metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO 2 at local areas in planar VO 2 /TiO 2 device configuration under external biasing. After each resistive switching, different VO 2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO 2 as the key material of neuromorphic computing.
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