Electronic synapses with near-linear weight update using MoS2/graphene memristors

Abstract

Emulating the human brain's circuitry composed of neurons and synapses is an emerging area of research in mitigating the “von Neumann bottleneck” in present computer architectures. The building block of these neuromorphic systems—the synapse—is commonly realized with oxide-based or phase change material-based devices, whose operation is limited by high programming currents and high reset currents. In this work, we have realized nonvolatile resistive switching MoS2/graphene devices that exhibit multiple conductance states at low operating currents. The MoS2/graphene devices exhibit essential synaptic behaviors, such as short and long-term potentiation, long-term depression, and the spike timing dependent plasticity learning rule. Most importantly, they exhibit a near-linear synaptic weight update, without any abrupt reset process, allowing their use in unsupervised learning applications. These electronic synapses are built with chemical vapor deposited MoS2 and graphene, demonstrating potential for large-scale realizations of machine learning hardware.