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@PHDTHESIS{Chekol:1021158,
      author       = {Chekol, Solomon},
      title        = {{U}nveiling the relaxation dynamics of {A}g/{H}f{O}2 based
                      diffusive memristors for use in neuromorphic computing},
      volume       = {101},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2024-00604},
      isbn         = {978-3-95806-729-5},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {x, 185},
      year         = {2023},
      note         = {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
                      performedusing 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.},
      cin          = {PGI-7},
      cid          = {I:(DE-Juel1)PGI-7-20110106},
      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)},
      pid          = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0398K},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2024-00604},
      url          = {https://juser.fz-juelich.de/record/1021158},
}