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@PHDTHESIS{Schnieders:1021101,
author = {Schnieders, Kristoffer},
othercontributors = {Waser, Rainer and Knoch, Joachim},
title = {{S}tatistische {C}harakterisierung und {M}odellierung von
memristiven {B}auelementen},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-09470},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2025},
abstract = {Recently, large language models (LLMs), such as ChatGPT,
have demonstrated significant potential as valuable tools
for facilitating a wide range of tasks. Consequently, a
restructuring of many professions due to LLMs appears
unavoidable. In parallel, concerns regarding the energy
consumption and computational complexity of LLM training and
inference have emerged. The power consumption of data-heavy
computational tasks, such as LLM training, is partially
attributed to the limitations of conventional von Neumann
architecture. In this architecture, the central processing
unit (CPU) and memory unit are spatially separated, leading
to increased latency and energy consumption. This so-called
memory wall, or von Neumann bottleneck, can be circumvented
by implementing brain-inspired computational architectures
that integrate computing and memory tasks within a
computing-in-memory unit (CMU). Memristive devices hold
promise as key components in CMUs, as they are capable of
performing both computational and memory tasks. One
promising class of memristive devices is valence change
mechanism (VCM) devices, which exhibit excellent endurance,
stackability, CMOS compatibility, and low energy
consumption. However, the intrinsic variability of these
devices remains a substantial hurdle. In particular, read
noise phenomena threaten the computational accuracy and
memory function of VCM devices, as the initially programmed
state can vary over time. Therefore, a deep understanding of
this phenomenon and strategies for mitigating its effects
are essential to align VCM devices with appropriate
applications. This thesis adopts a multi-level approach to
investigate read noise and its impact on application-level
performance. The content spans from verifying correlations
between material properties and read noise, derived from
theoretical physics, and investigating read noise
engineering approaches, to developing applications in which
device type and noise characteristics are optimally matched.
First, this thesis compares the characteristics of read
noise as a function of switching material and switching
modes of VCM devices. Next, methods to mitigate read noise
are explored, concluding with a discussion on potential
applications and statistical modeling. Initially, physical
explanations for read noise are validated and their
contributions quantified. The influence of material
properties on the read noise characteristics is investigated
by comparing VCM devices based on different switching
materials. The results link the read noise characteristics
to the energy gap between the conduction band (CB) and
oxygen defect states. For materials with small gaps,
electrons cross the Schottky barrier by tunneling from the
CB (Type 1), whereas for materials with larger gaps,
electrons tunnel from defect states (Type 2). Type 1 devices
exhibit low read noise, while Type 2 devices display
stochastic, abrupt current jumps of varying amplitude. These
jumps are attributed to the dislocation of oxygen vacancies
near the Schottky barrier. Building on these insights, the
next section examines how switching modes modulate read
noise characteristics. For Type 1 materials, few noise
events are observed in the filamentary switching mode, while
the area-dependent mode consistently exhibits low noise
amplitudes. For both switching modes, read noise remains
significantly lower than that of Type 2 devices. To address
the pronounced read noise in Type 2 materials, the
tunability of read noise via device fabrication and read
parameter variation is investigated. This includes modifying
fabrication processes and altering read voltages. The read
noise of thermally oxidized $TaO_x$ devices is measured
across a range of read voltages. It is observed that the
frequency and amplitude of noise events increase with the
duration and temperature of annealing. Furthermore, the read
noise amplitude increases with higher read voltages in set
polarity, while relative read noise remains nearly constant
for all reset polarities. Finally, potential applications of
VCM devices are analyzed with an emphasis on read noise
characteristics. The state instability of $TaO_x-based$ 1R
and 1T1R devices is evaluated. The read noise is found to be
the dominant contributor to state instability. The number of
practically achievable states is assessed, and instability
data is analyzed using statistical methods to ensure
reliable interpretation. The application chapter explores
how read noise impacts VCM device applications. To this end,
a statistical array model is developed to simulate the state
instability of VCM-based 1R and 1T1R arrays. This model
identifies key properties essential for the reliable
operation of large-scale VCM crossbar arrays while
significantly reducing simulation runtimes compared to
conventional compact models. Additionally, a read
noise-based true random number generator is introduced. This
generator showcases a method for harnessing the intrinsic
randomness of VCM devices, highlighting an additional
potential application of VCM arrays integrated within CMUs.},
cin = {611610},
ddc = {621.3},
cid = {$I:(DE-82)611610_20140620$},
pnm = {BMBF 16ME0398K - Verbundprojekt: Neuro-inspirierte
Technologien der künstlichen Intelligenz für die
Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)},
pid = {G:(DE-82)BMBF-16ME0398K},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-09470},
url = {https://publications.rwth-aachen.de/record/1021101},
}
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