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

doi:10.1021/acsami.8b04685

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

ACS Appl Mater Interfaces. 2018 Jun 20;10(24):20237-20243. doi: 10.1021/acsami.8b04685. Epub 2018 Jun 11.

Authors

Bo Liu¹, Zhiwei Liu¹, In-Shiang Chiu, MengFu Di², YongRen Wu³, Jer-Chyi Wang, Tuo-Hung Hou⁴, Chao-Sung Lai

Affiliations

¹ State Key Laboratory of Electronic Thin Films and Integrate Devices , University of Electronic Science and Technology of China , Chengdu 610054 , China.
² Department of Electrical and Computer Engineering , University of California , Riverside , California 92521 , United States.
³ Integrated Service Technology , Shanghai , China.
⁴ Department of Electronics Engineering and Institute of Electronics , National Chiao Tung University , Hsinchu , 300 , Taiwan.

PMID: 29873237
DOI: 10.1021/acsami.8b04685

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.

Keywords: artificial synapses; below femtojoule spiking energy; graphene electrode; neuromorphic memristor; programmable metaplasticity; spike-timing dependent plasticity.