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@PHDTHESIS{Hennen:961804,
      author       = {Hennen, Tyler Aaron},
      othercontributors = {Waser, Rainer and Gemmeke, Tobias},
      title        = {{H}arnessing stochasticity and negative differential
                      resistance for unconventional computation},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-07044},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2023},
      abstract     = {Recently, there has been a resurgence of interest in
                      materials with unusual electronic properties such as strong
                      nonlinearity, hysteresis, and memory. This interest is due
                      in part to the end of Moore scaling as well as the emergence
                      of novel computing architectures. Currently, computational
                      performance is limited by the memory bottleneck, as physical
                      memory is not fast or large enough to feed the central
                      processing unit (CPU) pipeline. One alternative is to
                      introduce a new tier of memory that must be substantially
                      faster and more scalable than existing Flash storage.
                      Another approach is to develop schemes that take advantage
                      of in-memory computation, as in the brain-inspired concepts
                      of neuromorphic computing (NC). To reach their full
                      potential, each of these strategies rely on the ability of
                      new classes of memory technologies to exploit physical
                      mechanisms yet to be fully harnessed on an industrial level.
                      This dissertation contains an investigation of two such
                      nascent nanotechnologies in the category of resistive
                      switching (RS). The first, redox-based resistive random
                      access memory (ReRAM), is capable of mimicking biological
                      synapses by allowing storage of large numbers of
                      interconnected and continuously adaptable resistance values.
                      The second technology is based on Cr-doped V₂O₃, a
                      correlated-electron material for which electronic control of
                      Mott insulator-to-metal transitions potentially offers a
                      fast and durable way to emulate the dynamical behavior of
                      neurons. Here, we apply reimagined methods and analysis of
                      electrical measurement to these synaptic and neuronal
                      devices. The newly acquired data sheds further light on the
                      nature of the resistance transitions and is used to design
                      physically validated device models for embedding in
                      large-scale neuromorphic simulations. The measurement
                      circuitry developed here addresses long-standing challenges
                      in the external stabilization of device test structures, and
                      allows (I, V) switching curves to be captured eight orders
                      of magnitude faster than with commercially available
                      equipment, while causing significantly less electrical
                      stress to the measured devices. Applying the measurement
                      system, we introduce a new stochastic device model for
                      solid-state synapses that is trained on a mass quantity of
                      statistical measurement data of ReRAM. This model enables
                      extremely fast (> 10⁸ OPS) and accurate simulations of
                      large synaptic arrays (> 10⁹ cells) and provides a
                      powerful new tool for statistical analysis of resistive
                      switching data. Next, we identify an electro-thermal
                      mechanism behind the negative differential resistance (NDR)
                      and neuronal dynamics observed in (V₁₋ₓCrₓ)₂O₃
                      nanodevices. We show fast volatile switching (< 10 ns), high
                      switching endurance (> 10¹² cycles), and favorable scaling
                      characteristics in this promising Mott insulating material.
                      A coexisting non-volatile (NV) mechanism is investigated and
                      the conditional occurrence of filamentation in the devices
                      is linked to circuit instabilities, with wider implications
                      for NV switching in other RS materials. The
                      (V₁₋ₓCrₓ)₂O₃ study culminates in a physical
                      model that covers the scaling behavior and threshold
                      adaptability, and is closely fit to observed oscillatory
                      data.},
      cin          = {611610},
      ddc          = {621.3},
      cid          = {$I:(DE-82)611610_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-07044},
      url          = {https://publications.rwth-aachen.de/record/961804},
}