Graphene–ferroelectric transistors as complementary synapses for supervised learning in spiking neural network
Release time:2021-09-08
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Indexed by:Journal paper
Journal:NPJ 2D Materials & Applications
Included Journals:SCI
Discipline:Engineering
First-Level Discipline:Electronic Science And Technology
Document Type:J
Volume:3
Page Number:31
Key Words:graphene, organic ferroelectric, transistor, complementary synapse, ReSuMe
DOI number:10.1038/s41699-019-0114-6
Date of Publication:2019-07-08
Impact Factor:11.0
Abstract:The hardware design of supervised learning (SL) in spiking neural network (SNN) prefers 3-terminal memristive synapses, where the third terminal is used to impose supervise signals. In this work we address this demand by fabricating graphene transistor gated through organic ferroelectrics of polyvinylidenefluoride. Through gate tuning not only is the nonvolatile and continuous change of graphene channel conductance demonstrated, but also the transition between electron-dominated and hole-dominated transport. By exploiting the adjustable bipolar characteristic, the graphene–ferroelectric transistor can be electrically reconfigured as potentiative or depressive synapse and in this way complementary synapses are realized. The complementary synapse and neuron circuit is then constructed to execute remote supervise method (ReSuMe) of SNN, and quick convergence to successful learning is found through network-level simulation when applying to a SL task of classifying 3 × 3-pixel images. The presented design of graphene–ferroelectric transistor-based complementary synapses and quantitative simulation may indicate a potential approach to
hardware implementation of SL in SNN.
