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@ARTICLE{Bengel:893059,
      author       = {Bengel, Christopher and Cüppers, Felix and Payvand, Melika
                      and Dittmann, Regina and Waser, R. and Hoffmann-Eifert,
                      Susanne and Menzel, Stephan},
      title        = {{U}tilizing the {S}witching {S}tochasticity of
                      {H}f{O}2/{T}i{O}x-{B}ased {R}e{RAM} {D}evices and the
                      {C}oncept of {M}ultiple {D}evice {S}ynapses for the
                      {C}lassification of {O}verlapping and {N}oisy {P}atterns},
      journal      = {Frontiers in neuroscience},
      volume       = {15},
      issn         = {1662-453X},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2021-02533},
      pages        = {661856},
      year         = {2021},
      abstract     = {With the arrival of the Internet of Things (IoT) and the
                      challenges arising from Big Data, neuromorphic chip concepts
                      are seen as key solutions for coping with the massive amount
                      of unstructured data streams by moving the computation
                      closer to the sensors, the so-called “edge computing.”
                      Augmenting these chips with emerging memory technologies
                      enables these edge devices with non-volatile and adaptive
                      properties which are desirable for low power and online
                      learning operations. However, an energy- and area-efficient
                      realization of these systems requires disruptive hardware
                      changes. Memristor-based solutions for these concepts are in
                      the focus of research and industry due to their low-power
                      and high-density online learning potential. Specifically,
                      the filamentary-type valence change mechanism (VCM memories)
                      have shown to be a promising candidate In consequence,
                      physical models capturing a broad spectrum of experimentally
                      observed features such as the pronounced cycle-to-cycle
                      (c2c) and device-to-device (d2d) variability are required
                      for accurate evaluation of the proposed concepts. In this
                      study, we present an in-depth experimental analysis of d2d
                      and c2c variability of filamentary-type bipolar switching
                      HfO2/TiOx nano-sized crossbar devices and match the
                      experimentally observed variabilities to our physically
                      motivated JART VCM compact model. Based on this approach, we
                      evaluate the concept of parallel operation of devices as a
                      synapse both experimentally and theoretically. These
                      parallel synapses form a synaptic array which is at the core
                      of neuromorphic chips. We exploit the c2c variability of
                      these devices for stochastic online learning which has shown
                      to increase the effective bit precision of the devices.
                      Finally, we demonstrate that stochastic switching features
                      for a pattern classification task that can be employed in an
                      online learning neural network.},
      cin          = {PGI-7 / JARA-FIT / PGI-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)PGI-7-20110106 / $I:(DE-82)080009_20140620$ /
                      I:(DE-Juel1)PGI-10-20170113},
      pnm          = {523 - Neuromorphic Computing and Network Dynamics
                      (POF4-523) / 5233 - Memristive Materials and Devices
                      (POF4-523) / Advanced Computing Architectures
                      $(aca_20190115)$ / MNEMOSENE - Computation-in-memory
                      architecture based on resistive devices (780215) /
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC -, Teilvorhaben: Forschungszentrum Jülich
                      (16ES1133K)},
      pid          = {G:(DE-HGF)POF4-523 / G:(DE-HGF)POF4-5233 /
                      $G:(DE-Juel1)aca_20190115$ / G:(EU-Grant)780215 /
                      G:(BMBF)16ES1133K},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34163323},
      UT           = {WOS:000663741500001},
      doi          = {10.3389/fnins.2021.661856},
      url          = {https://juser.fz-juelich.de/record/893059},
}