Thermally-driven Multilevel Non-volatile Memory with Monolayer MoS2 for Neuro-inspired Artificial Learning

Abstract

The demands of modern electronic components require advanced computing platforms for efficient information processing to realize in-memory operations with a high density of data storage capabilities towards developing alternatives to von Neumann architectures. Herein, we demonstrate the multifunctionality of monolayer MoS2 mem-transistors which can be used as a high-geared intrinsic transistor at room temperature; however, at a high temperature (>350 K), they exhibit synaptic multi-level memory operations. The temperature-dependent memory mechanism is governed by interfacial physics, which solely depends on the gate field modulated ion dynamics and charge transfer at the MoS2/dielectric interface. We have proposed a non-volatile memory application using a single FET device where thermal energy can be ventured to aid the memory functions with multi-level (3-bit) storage capabilities. Furthermore, our devices exhibit linear and symmetry in conductance weight updates when subjected to electrical potentiation and depression. This feature has enabled us to attain a high classification accuracy while training and testing the Modified National Institute of Standards and Technology datasets through artificial neural network simulation. This work paves the way for new avenues in 2D semiconductors toward reliable data processing and storage with high-packing density arrays for brain-inspired artificial learning.