Spiking neural network

Electrical engineering

Neuromorphic engineering

Neuroscience

Nanotechnology

Transistor

Synapse

Engineering

Dielectric

Voltage

Artificial intelligence

Psychology

MNIST database

Materials science

Electronic engineering

Graphene

Computer science

Optoelectronics

Artificial neural network

Ferroelectricity

Novel complementary graphene-ferroelectric transistors (C-GFTs) based synapses are proposed and experimentally demonstrated for the first time. By exploiting the unique zero-bandgap property of graphene, GFT based synapses can be dynamically reconfigured between potentiative and depressive (PD) modes corresponding to hole-and electron dominated transport in graphene channels, respectively. Both modes demonstrate excellent linearity, small (2%) cycle-to-cycle variation and > 32 levels when used as synapses. By configurating the PD modes into a pair of C-GFTs, the hardware architecture of spiking neural networks (SNNs) can be substantially innovated, where the complicated circuitry previously required for supervised learning is now completely removed. With C-GFTs, a synapse footprint of 100 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a power consumption of 8 pJ/per operation are demonstrated in the MNIST learning task.

Complementary Graphene-Ferroelectric Transistors (C-GFTs) as Synapses with Modulatable Plasticity for Supervised Learning