Thermally Driven Multilevel Non-Volatile Memory with Monolayer MoS2 for Brain-Inspired Artificial Learning

• Published in: ACS applied materials & interfaces 2023-08, Vol.15 (30), p.36527-36538
• Main Authors: Mallik, Sameer Kumar, Padhan, Roshan, Sahu, Mousam Charan, Roy, Suman, Pradhan, Gopal K., Sahoo, Prasana Kumar, Dash, Saroj Prasad, Sahoo, Satyaprakash
• Format: Article
• Language: English
• Subjects: Functional Inorganic Materials and Devices
• ISSN: 1944-8244; 1944-8252
• DOI: 10.1021/acsami.3c06336

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 toward developing alternatives to von Neumann architectures. Herein, we demonstrate the multifunctionality of monolayer MoS2 memtransistors, which can be used as a high-geared intrinsic transistor at room temperature; however, at a high temperature (>350 K), they exhibit synaptic multilevel 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 Field Effect Transistor (FET) device where thermal energy can be ventured to aid the memory functions with multilevel (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 toward reliable data processing and storage using 2D semiconductors with high-packing density arrays for brain-inspired artificial learning.