4.8 Article

Tunable synaptic behavior realized in C3N composite based memristor

Journal

NANO ENERGY
Volume 58, Issue -, Pages 293-303

Publisher

ELSEVIER
DOI: 10.1016/j.nanoen.2019.01.045

Keywords

Artificial synapse; Memristor; Proton conduction; Two-dimentional; C3N

Funding

  1. Natural Science Foundation of China [61604097, 61601305, 11774368, 11704204, 11227902]
  2. Shenzhen Science and Technology Innovation Commission [JCYJ20170818143618288, JCYJ20170302145229928, JCYJ20170302151653768]
  3. Shenzhen Peacock Technological Innovation Project [KQJSCX20170727100433270, KQJSCX20170327150812967]
  4. Department of Education of Guangdong Province [2016KTSCX120]
  5. Guangdong Provincial Department of Science and Technology [2018B030306028, 2017TQ04X082]
  6. China Postdoctoral Science Foundation [BX201700271, 2017M621564]
  7. Shanghai Science and Technology Committee [18511110600]

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Inspired by the parallel performing of storage and processing information in biological synapse, artificial synapse that can control ionic and protonic currents are ideal means for attracted tremendous attention as a promising option to break the von Neumann bottleneck. Here we demonstrate an artificial synapse with tunable synaptic behavior based on solution-processed two-dimensional (2D) C3N/polyvinylpyrrolidone (PVPy). The proton modulated memristive characteristics of synaptic device are verified by ambient pressure X-ray photoelectron spectroscopy under different H2O gas atmosphere. The highly proton conducting of C3N/PVPy matrix stems from the hydrogen bonding network between C3N and PVPy, as well as the large amount of ordered nitrogen atoms in C3N. The artificial synapse ensure a direct imitation of short-term and long-term plasticity in biological synapses including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), paired-pulse depression (PPD), PPF following PPD and post-tetanic potentiation (PTP). The C3N/PVPy matrix-based memristor which can mimic the synapse cleft based on proton conducting mechanism may find further applications in artificial intelligence.

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