skip to main content


This content will become publicly available on August 1, 2024

Title: Dataset of optical and electronic properties for MoS2-graphene vertical heterostructures and MoS2-graphene-Au heterointerfaces
Award ID(s):
2018900
NSF-PAR ID:
10487139
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Data in Brief
Volume:
49
Issue:
C
ISSN:
2352-3409
Page Range / eLocation ID:
109341
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS2/SiOx/Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS2-based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS2/SiOx/Ni synapses, when integrated with a MoS2-based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS2as a viable platform for all-memristive SNNs.

     
    more » « less