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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Neuromorphic computing offers a new paradigm to overcome the von Neumann bottleneck by mimicking brain structures.
  • Electronic synaptic devices are crucial for realizing efficient neuromorphic systems.
  • Graphene oxide (GO) presents a promising material for developing synaptic devices.

Purpose of the Study:

  • To develop and investigate a graphene oxide (GO) based memristor device as a functional artificial synapse.
  • To evaluate the synaptic learning behaviors and non-volatile characteristics of the fabricated memristor.

Main Methods:

  • Fabrication of a silver/graphene oxide/fluorine-doped tin oxide (Ag/GO/FTO) memristor device.
  • Characterization of analog memory, potentiation, and depression behaviors.
  • Engineering pre- and post-synaptic spikes to mimic the spike-timing-dependent-plasticity (STDP) learning rule.
  • Assessment of non-volatile properties including endurance, retentivity, and multilevel switching.

Main Results:

  • The Ag/GO/FTO memristor successfully demonstrated essential synaptic learning behaviors, including analog memory, potentiation, and depression.
  • The device effectively mimicked the spike-timing-dependent-plasticity (STDP) learning rule through controlled pre- and post-synaptic spike engineering.
  • Excellent non-volatile properties such as high endurance, stable retentivity, and multilevel switching capabilities were observed.

Conclusions:

  • The developed Ag/GO/FTO memristor device shows significant potential as an artificial synapse for neuromorphic computing.
  • The demonstrated synaptic functions and non-volatile characteristics make it a promising candidate for future brain-inspired computing applications.