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@ARTICLE{Ntinas:907570,
author = {Ntinas, Vasileios and Ascoli, Alon and Messaris, Ioannis
and Wang, Yongmin and Rana, Vikas and Menzel, Stephan and
Tetzlaff, Ronald},
title = {{T}oward {S}implified {P}hysics-{B}ased {M}emristor
{M}odeling of {V}alence {C}hange {M}echanism {D}evices},
journal = {IEEE transactions on circuits and systems / 2},
volume = {69},
number = {5},
issn = {1057-7130},
address = {New York, NY},
publisher = {IEEE},
reportid = {FZJ-2022-02087},
pages = {2473 - 2477},
year = {2022},
abstract = {Memristors are promising nanoelectronic devices for the
implementation of future AI-driven sensor-processor
electronic systems, which are essential for the ongoing
digitalization of our world. Accurate and computationally
cost-effective models for the manufactured memristors are
essential for the design of such systems, especially for the
simulation of large circuits. In this brief we address the
simplification of the JART memristor model, a generic
physics-based model of Valence Change Mechanism (VCM)
memristors which accurately describes the dynamic behavior
of fabricated memristor devices. Furthermore, the proposed
model and simplification methodology have the potential to
capture the dynamics of a wide range of memristor devices.
Importantly, the implicit description of the current through
the memristor is replaced by an explicit mathematical
relationship. The proper reproduction of memristor dynamics,
verified by applying the system-theoretic Dynamic Route Map
(DRM) graphical analysis tool, applicable to first-order
systems, can be observed through the proposed simplified
model and enables the time-efficient simulation of large
arrays of VCM devices.},
cin = {PGI-7 / JARA-FIT / PGI-10},
ddc = {620},
cid = {I:(DE-Juel1)PGI-7-20110106 / $I:(DE-82)080009_20140620$ /
I:(DE-Juel1)PGI-10-20170113},
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) /
BMBF-16ME0399 - Verbundprojekt: Neuro-inspirierte
Technologien der künstlichen Intelligenz für die
Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0399) / SFB
917 B01 - Schnelle transiente, elektrische Analyse von
resistiven Schaltphänomenen (B01) (202259360) / ACA -
Advanced Computing Architectures (SO-092)},
pid = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0398K /
G:(DE-82)BMBF-16ME0399 / G:(GEPRIS)202259360 /
G:(DE-HGF)SO-092},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000790814000021},
doi = {10.1109/TCSII.2022.3160304},
url = {https://juser.fz-juelich.de/record/907570},
}