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@ARTICLE{Funck:894111,
author = {Funck, Carsten and Menzel, Stephan},
title = {{A} comprehensive model of electron conduction in
oxide-based memristive devices},
journal = {ACS applied electronic materials},
volume = {3},
number = {9},
issn = {2637-6113},
address = {Washington, DC},
publisher = {ACS Publications},
reportid = {FZJ-2021-03044},
pages = {3674 - 3692},
year = {2021},
abstract = {Memristive devices are two-terminal devices that can change
their resistance state upon application of appropriate
voltage stimuli. The resistance can be tuned over a wide
resistance range enabling applications such as multibit data
storage or analog computing-in-memory concepts. One of the
most promising classes of memristive devices is based on the
valence change mechanism in oxide-based devices. In these
devices, a configurational change of oxygen defects, i.e.
oxygen vacancies, leads to the change of the device
resistance. A microscopic understanding of the conduction is
necessary in order to design memristive devices with
specific resistance properties. In this paper, we discuss
the conduction mechanism proposed in the literature and
propose a comprehensive, microscopic model of the conduction
mechanism in this class of devices. To develop this
microscopic picture of the conduction, ab initio simulation
models are developed. These simulations suggest two
different types of conduction, which are both limited by a
tunneling through the Schottky barrier at the metal
electrode contact. The difference between the two conduction
mechanisms is the following: for the first type, the
electrons tunnel into the conduction band and, in the second
type, into the vacancy defect states. These two types of
conduction differ in their current voltage relation, which
has been detected experimentally. The origin of the
resistive switching is identical for the two types of
conduction and is based on a modification of the tunneling
distance due to the oxygen vacancy induced screening of the
Schottky barrier. This understanding may help to design
optimized devices in terms of the dynamic resistance range
for specific applications.},
cin = {PGI-7},
ddc = {620},
cid = {I:(DE-Juel1)PGI-7-20110106},
pnm = {5233 - Memristive Materials and Devices (POF4-523) /
Verbundprojekt: Neuro-inspirierte Technologien der
künstlichen Intelligenz für die Elektronik der Zukunft -
NEUROTEC -, Teilvorhaben: Forschungszentrum Jülich
(16ES1133K) / BMBF-16ES1134 - Verbundprojekt:
Neuro-inspirierte Technologien der künstlichen Intelligenz
für die Elektronik der Zukunft - NEUROTEC -
(BMBF-16ES1134)},
pid = {G:(DE-HGF)POF4-5233 / G:(BMBF)16ES1133K /
G:(DE-82)BMBF-16ES1134},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000703541500001},
doi = {10.1021/acsaelm.1c00398},
url = {https://juser.fz-juelich.de/record/894111},
}