Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-Based Neuromorphic Memristor

Bo Liu, Zhiwei Liu, In Shiang Chiu, Mengfu Di, Yongren Wu, Jer Chyi Wang, Tuo-Hung Hou*, Chao Sung Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Memristors with rich interior dynamics of ion migration are promising for mimicking various biological synaptic functions in neuromorphic hardware systems. A graphene-based memristor shows an extremely low energy consumption of less than a femtojoule per spike, by taking advantage of weak surface van der Waals interaction of graphene. The device also shows an intriguing programmable metaplasticity property in which the synaptic plasticity depends on the history of the stimuli and yet allows rapid reconfiguration via an immediate stimulus. This graphene-based memristor could be a promising building block toward designing highly versatile and extremely energy efficient neuromorphic computing systems.

Original languageEnglish
Pages (from-to)20237-20243
Number of pages7
JournalACS Applied Materials and Interfaces
Volume10
Issue number24
DOIs
StatePublished - 20 Jun 2018

Keywords

  • artificial synapses
  • below femtojoule spiking energy
  • graphene electrode
  • neuromorphic memristor
  • programmable metaplasticity
  • spike-timing dependent plasticity

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