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Thermally Driven Multilevel Non-Volatile Memory with Monolayer MoS2 for Brain-Inspired Artificial Learning.
Sameer Kumar Mallik1,2, Roshan Padhan1,2, Mousam Charan Sahu1,2
1Laboratory for Low Dimensional Materials, Institute of Physics, Bhubaneswar 751005, India.
ACS Applied Materials & Interfaces
|July 19, 2023
Summary
Monolayer MoS2 memtransistors function as transistors at room temperature but exhibit synaptic memory operations at high temperatures. This 2D semiconductor technology enables efficient data processing and storage for brain-inspired computing.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Nanotechnology
Background:
- Modern electronics demand advanced computing platforms for in-memory operations and high-density data storage.
- There is a need for alternatives to traditional von Neumann architectures to meet these demands.
Purpose of the Study:
- To demonstrate the multifunctionality of monolayer MoS2 memtransistors for advanced computing applications.
- To explore the temperature-dependent memory mechanisms and potential for non-volatile memory applications.
Main Methods:
- Fabrication and characterization of monolayer MoS2 memtransistors.
- Investigation of device performance at varying temperatures (room temperature and >350 K).
- Analysis of interfacial physics, ion dynamics, and charge transfer governing memory mechanisms.
Main Results:
- Monolayer MoS2 memtransistors exhibit intrinsic transistor behavior at room temperature.
- At temperatures above 350 K, devices display synaptic multilevel memory operations.
- Demonstrated 3-bit multilevel storage capability in a single Field Effect Transistor (FET) device.
- Achieved high classification accuracy in artificial neural network simulations using MoS2 memtransistors for data processing.
Conclusions:
- The temperature-dependent interfacial physics in MoS2 memtransistors enables versatile functionalities for computing.
- These 2D semiconductor devices offer a promising pathway for high-density data storage and brain-inspired artificial learning.
- The proposed non-volatile memory application leverages thermal energy for enhanced memory functions.
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