Controlling Native Oxidation of HfS₂ for 2D Materials Based Flash Memory and Artificial Synapse.

Area of Science:

• Materials Science
• Nanotechnology
• Neuroscience

Background:

• Two-dimensional (2D) materials are key for energy-efficient brain-inspired computing.
• Current 2D devices face challenges with deposited dielectric layers, complicating fabrication and compatibility.

Purpose of the Study:

• To develop a simplified method for integrating high-k dielectrics into 2D material-based devices.
• To demonstrate the potential of these devices as artificial synapses for neural networks.

Main Methods:

• A controllable oxidation process was used to convert 2D HfS2 into native HfO2, forming a HfO2/HfS2 heterostructure.
• This heterostructure was integrated into a flash memory device to test its properties.
• The device's performance as an artificial synapse was evaluated, and its accuracy in a simulated neural network was assessed.

Main Results:

• The HfO2/HfS2 heterostructure demonstrated excellent insulating properties and a clean interface.
• The flash memory device exhibited a high on/off current ratio (~10^5), a large memory window (>60 V), good endurance, and long retention time (>10^3 s).
• The device successfully emulated artificial synaptic functions with linear and symmetric conductance changes, achieving ~88% accuracy in MNIST recognition within a simulated neural network.

Conclusions:

• This work presents a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.
• The developed native oxide dielectric layer overcomes fabrication complexities associated with traditional methods.
• The demonstrated synaptic device performance shows significant promise for advancing brain-inspired computing and neuromorphic applications.