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Synaptic memory devices from CoO/Nb:SrTiO3 junction.

Le Zhao1,2, Jie Xu3, Xiantao Shang3

  • 1School of Control Science and Engineering, Institute of Biomedical Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China.

Royal Society Open Science
|June 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel CoO/Nb:SrTiO3 heterojunction memristor for artificial synapses. It demonstrates reliable synaptic plasticity, offering lower energy consumption for future neurocomputation systems.

Keywords:
conductance modulationnon-volatile memristorspike-timing-dependent plasticitysynaptic device

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Non-volatile memristors are crucial for hardware-based neurocomputation due to their ability to mimic biological synapses.
  • Current research focuses on optimizing memristor performance for lower energy consumption and enhanced biological plausibility.

Purpose of the Study:

  • To present a novel non-volatile memristor design utilizing a CoO/Nb:SrTiO3 heterojunction.
  • To investigate the device's potential for emulating synaptic functions and implementing learning rules.

Main Methods:

  • Fabrication of a CoO/Nb:SrTiO3 heterojunction memristor.
  • Characterization of resistivity switching behaviors attributed to oxygen vacancy migration and charge trapping/detrapping.
  • Implementation of voltage pulse stimulations to achieve gradual conductance modulation.

Main Results:

  • The memristor exhibits intrinsic resistivity switching behaviors at the heterojunction interface.
  • Carrier trapping and detrapping levels are controllable via voltage amplitude regulation.
  • Successful implementation of spike-timing-dependent plasticity (STDP), a key Hebbian learning rule.

Conclusions:

  • The CoO/Nb:SrTiO3 heterojunction shows promise for creating artificial synapses.
  • This device offers potential advantages over filamentary memristors, including reduced energy consumption and improved reliability for large-scale neuromorphic systems.