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@ARTICLE{Gutsche:894113,
author = {Gutsche, Alexander and Siegel, Sebastian and Zhang, Jinchao
and Hambsch, Sebastian and Dittmann, Regina},
title = {{E}xploring {A}rea-{D}ependent
{P}r0.7{C}a0.3{M}n{O}3-{B}ased {M}emristive {D}evices as
{S}ynapses in {S}piking and {A}rtificial {N}eural
{N}etworks},
journal = {Frontiers in neuroscience},
volume = {15},
issn = {1662-453X},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2021-03046},
pages = {661261},
year = {2021},
abstract = {Memristive devices are novel electronic devices, which
resistance can be tuned by an external voltage in a
non-volatile way. Due to their analog resistive switching
behavior, they are considered to emulate the behavior of
synapses in neuronal networks. In this work, we investigate
memristive devices based on the field-driven redox process
between the p-conducting Pr0.7Ca0.3MnO3 (PCMO) and different
tunnel barriers, namely, Al2O3, Ta2O5, and WO3. In contrast
to the more common filamentary-type switching devices, the
resistance range of these area-dependent switching devices
can be adapted to the requirements of the surrounding
circuit. We investigate the impact of the tunnel barrier
layer on the switching performance including area scaling of
the current and variability. Best performance with respect
to the resistance window and the variability is observed for
PCMO with a native Al2O3 tunnel oxide. For all different
layer stacks, we demonstrate a spike timing dependent
plasticity like behavior of the investigated PCMO cells.
Furthermore, we can also tune the resistance in an analog
fashion by repeated switching the device with voltage pulses
of the same amplitude and polarity. Both measurements
resemble the plasticity of biological synapses. We
investigate in detail the impact of different pulse heights
and pulse lengths on the shape of the stepwise SET and RESET
curves. We use these measurements as input for the
simulation of training and inference in a multilayer
perceptron for pattern recognition, to show the use of
PCMO-based ReRAM devices as weights in artificial neural
networks which are trained by gradient descent methods.
Based on this, we identify certain trends for the impact of
the applied voltages and pulse length on the resulting shape
of the measured curves and on the learning rate and accuracy
of the multilayer perceptron.},
cin = {PGI-7 / PGI-10 / JARA-FIT},
ddc = {610},
cid = {I:(DE-Juel1)PGI-7-20110106 / I:(DE-Juel1)PGI-10-20170113 /
$I:(DE-82)080009_20140620$},
pnm = {5233 - Memristive Materials and Devices (POF4-523) /
BMBF-16ME0398K - Verbundprojekt: Neuro-inspirierte
Technologien der künstlichen Intelligenz für die
Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K) /
ACA - Advanced Computing Architectures (SO-092) / DFG
project 167917811 - SFB 917: Resistiv schaltende
Chalkogenide für zukünftige Elektronikanwendungen:
Struktur, Kinetik und Bauelementskalierung "Nanoswitches"
(167917811)},
pid = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0398K /
G:(DE-HGF)SO-092 / G:(GEPRIS)167917811},
typ = {PUB:(DE-HGF)16},
pubmed = {34276286},
UT = {WOS:000674438100001},
doi = {10.3389/fnins.2021.661261},
url = {https://juser.fz-juelich.de/record/894113},
}