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@ARTICLE{Xi:907222,
      author       = {Xi, Fengben and Han, Yi and Grenmyr, Andreas and
                      Grutzmacher, Detlev and Zhao, Qing-Tai},
      title        = {{F}our-{T}erminal {F}erroelectric {S}chottky {B}arrier
                      {F}ield {E}ffect {T}ransistors as {A}rtificial {S}ynapses
                      for {N}euromorphic {A}pplications},
      journal      = {IEEE journal of the Electron Devices Society},
      volume       = {10},
      issn         = {2168-6734},
      address      = {[New York, NY]},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-01903},
      pages        = {569-574},
      year         = {2022},
      abstract     = {In this paper, artificial synapses based on four terminal
                      ferroelectric Schottky barrier field effect transistors
                      (FE-SBFETs) are experimentally demonstrated. The
                      ferroelectric polarization switching dynamics gradually
                      modulate the Schottky barriers, thus programming the device
                      conductance by applying negative or postive pulses to
                      imitate the excitation and inhibition behaviors of the
                      biological synapse. The excitatory post-synaptic current can
                      be modulated by the back-gate bias, enabling the
                      reconfiguration of the weight profile with high speed of 20
                      ns and low energy (< 1 fJ/spike) consumption. Besides, the
                      tunable long term potentiation and depression show high
                      endurance and very small cycle-to-cycle variations. Based on
                      the good linearity, high symmetricity and large dynamic
                      range of the synaptic weight updates, a high recognition
                      accuracy $(92.6\%)$ is achieved for handwritten digits by
                      multilayer perceptron artificial neural networks. These
                      findings demonstrate FE-SBFET has high potential as an ideal
                      synaptic component for the future intelligent neuromorphic
                      network.},
      cin          = {PGI-9},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)PGI-9-20110106},
      pnm          = {5234 - Emerging NC Architectures (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000836630300003},
      doi          = {10.1109/JEDS.2022.3166449},
      url          = {https://juser.fz-juelich.de/record/907222},
}