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@PHDTHESIS{Chekol:971953,
author = {Chekol, Solomon Amsalu},
othercontributors = {Waser, Rainer and Wuttig, Matthias},
title = {{U}nveiling the relaxation dynamics of ${A}g/{H}f{O}_{2}$
based diffusive memristors for use in neuromorphic
computing},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-09925},
pages = {1 Online-Ressource : Illustrationen, Diagramme},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2024; Dissertation, RWTH Aachen University, 2023},
abstract = {The rapid growth in volume and complexity of data and
transfer, driven by advancements in information technologies
such as artificial intelligence (AI), cloud computing, big
data, and machine learning, is placing significant demands
on computation power and speed. Traditional computing
architectures are facing challenges in meeting these demands
due to the Von Neumann bottleneck, which limits the data
transfer rate between the memory and the central processing
unit and causes high energy consumption. Today, neuromorphic
computing (NC) concepts that mimic the structure and
function of the biological brain are gaining popularity as
they promise energy-efficient and scalable computing
solutions. Currently, neuronal functionality is often
performed using a transistor-based neuron, which is area-
and energy-inefficient. Therefore, research in the "beyond
von Neumann" area is aimed at novel volatile switching
components with adjustable switching times, low power
consumption, and high scalability, which could potentially
be used as artificial neurons in NC circuits. These include
threshold-switching devices that switch abruptly from the
high-resistance state (HRS) to the low-resistance state
(LRS) at a defined voltage. As soon as the applied voltage
falls below a certain value, the cell relaxes back to the
initial HRS state. In particular, diffusive memristors built
from volatile electrochemical metallization (ECM) cells are
attracting attention in emerging NC areas such as temporal
encoding. These diffusive memristors consist of switching
layers made from oxides or chalcogenides sandwiched between
an electrochemically active electrode (e.g., Ag or Cu) and
an inert electrode (e.g., Pt metal). The cells can be
miniaturized down to the sub-micrometer range and the
switching itself relies on the formation and dissolution of
a metallic filament. Since the temporal behavior of
diffusive memristors is their main characteristic, it is of
crucial importance to understand the relaxation dynamics of
these devices from a physical perspective. This is a
prerequisite for optimizing and modulating the performance
of diffusive memristors, especially for applications
requiring precise control of switching times. Previous
approaches mainly describe the relaxation time as a function
of the given filament diameter while the filament growth
process is not considered. In contrast, the present work
takes a comprehensive approach based on the physical
description of filament formation and relaxation and a
possible dependence between the two. The goal is to develop
a deeper understanding of the relaxation dynamics of
diffusive memristors, with particular emphasis on the
influence of SET parameters on the structure and diameter of
the filament formed in each case. To this end, devices were
fabricated from an amorphous HfO2 layer of a few nanometers
thickness sandwiched between electrode layers of Ag and Pt.
For material influence studies, SiO2 as an electrolyte layer
and two other metals (TiN and W) as alternative counter
electrodes (CEs) were also investigated. By evaluating the
transient current response to applied voltage pulses, the
SET kinetics and relaxation dynamics of the cells were
comprehensively analyzed. This allowed the identification of
different mechanisms as rate-limiting steps for filament
formation. In particular, three processes, namely
nucleation, electron-transfer, and mixed processes
(electron-transfer and ion-migration), were identified as
rate-limiting steps during the SET process. It was shown
that the relaxation time strongly depends on the selected
SET parameters. This is explained by the different
mechanisms of filament formation during the SET event. A
slow SET process at low voltage results in a relatively
thick filament that takes long to self-rupture. In contrast,
fast SET processes at high voltage lead to dendritic
filaments with short relaxation times. This assertion is
consistent with previous Monte Carlo simulations for similar
ECM cells and is further supported by physics-based
simulations using the "JART ECM" model developed in
collaboration with IWE 2/RWTH. Furthermore, it has been
demonstrated that applying a higher voltage pulse amplitude
or longer duration leads to a substantial increase in
relaxation time. This could be due to a small residual
voltage drop across the diffusive memristor in the LRS,
which causes further growth of the filament after closure.
The new findings resulting from the combined analysis of SET
kinetics and relaxation dynamics underline the importance of
filament formation for relaxation behavior and provide
important information for optimal operating conditions of
these threshold switches in NC circuits. In addition, the
work addresses the influence of CE material on the SET
kinetics of diffusive memristors. For example, both TiN and
W CEs slow down the switching speed compared to the Pt
electrode. This is attributed to the different
electrocatalytic activity of the different metals in the
redox reaction. Oxide formation at the interface of
non-noble metals (i.e., TiN and W) is also included in the
discussion. An important effect for use in NC circuits is
the significant influence of a series resistor on the
relaxation behavior. It has been shown that the magnitude of
the series resistance can strongly influence the
relationship between relaxation time and pulse voltage
amplitude. This can be attributed to the highly non-linear
nature of the SET kinetics and the role that a residual
voltage drop across the device in the ON-state plays in
promoting continued growth and strengthening of the
conductive filament.In summary, this work shows that
diffusive memristors based on volatile ECM cells have a high
potential for use as artificial neurons and further
applications in the field of NC. The obtained results
contribute to a deeper physical understanding of the
interplay between filament formation and relaxation and can
be directly transferred to the optimization of the
operational conditions of diffusive memristors in
neuromorphic circuits. This represents an important step
towards the realization of energy-efficient NC solutions. In
addition, the work classifies volatile devices in terms of
different adjustable relaxation times against the background
of various NC applications.},
cin = {520000 / 611610},
ddc = {620},
cid = {$I:(DE-82)520000_20140620$ / $I:(DE-82)611610_20140620$},
pnm = {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) /
BMBF 03ZU1106AB - NeuroSys: "Memristor Crossbar
Architekturen (Projekt A) - B" (BMBF-03ZU1106AB)},
pid = {G:(DE-82)BMBF-16ME0398K / G:(DE-82)BMBF-16ME0399 /
G:(DE-Juel1)BMBF-03ZU1106AB},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2023-09925},
url = {https://publications.rwth-aachen.de/record/971953},
}
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