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@ARTICLE{Siemon:873814,
      author       = {Siemon, A. and Ferch, S. and Heittmann, A. and Waser, R.
                      and Wouters, D. J. and Menzel, S.},
      title        = {{A}nalyses of a 1-layer neuromorphic network using
                      memristive devices with non-continuous resistance levels},
      journal      = {APL materials},
      volume       = {7},
      number       = {9},
      issn         = {2166-532X},
      address      = {Melville, NY},
      publisher    = {AIP Publ.},
      reportid     = {FZJ-2020-01019},
      pages        = {091110 -},
      year         = {2019},
      abstract     = {The emerging nonvolatile memory technology of redox-based
                      resistive switching (RS) devices is not only a promising
                      candidate for future high density memories but also for
                      computational and neuromorphic applications. In neuromorphic
                      as well as in memory applications, RS devices are configured
                      in nanocrossbar arrays, which are controlled by CMOS
                      circuits. With those hybrid systems, brain-inspired
                      artificial neural networks can be built up and trained by
                      using a learning algorithm. First works on hardware
                      implementation using relatively large and high current level
                      RS devices are already published. In this work, the
                      influence of small and low current level devices showing
                      noncontinuous resistance levels on neuromorphic networks is
                      studied. To this end, a well-established physical-based
                      Verilog A model is modified to offer continuous and discrete
                      conduction. With this model, a simple one-layer neuromorphic
                      network is simulated to get a first insight and
                      understanding of this problem using a backpropagation
                      algorithm based on the steepest descent method},
      cin          = {PGI-7 / JARA-FIT / PGI-10},
      ddc          = {600},
      cid          = {I:(DE-Juel1)PGI-7-20110106 / $I:(DE-82)080009_20140620$ /
                      I:(DE-Juel1)PGI-10-20170113},
      pnm          = {521 - Controlling Electron Charge-Based Phenomena
                      (POF3-521)},
      pid          = {G:(DE-HGF)POF3-521},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000489245900006},
      doi          = {10.1063/1.5108658},
      url          = {https://juser.fz-juelich.de/record/873814},
}