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

Abstract

Two-dimensional (2D) materials based artificial synapses are important building blocks for the brain-inspired computing systems that are promising in handling large amounts of informational data with high energy-efficiency in the future. However, 2D devices usually rely on deposited or transferred insulators as the dielectric layer, resulting in various challenges in device compatibility and fabrication complexity. Here, we demonstrate a controllable and reliable oxidation process to turn 2D semiconductor HfS₂ into native oxide, HfO_x, which shows good insulating property and clean interface with HfS₂. We then incorporate the HfO_x/HfS₂ heterostructure into a flash memory device, achieving a high on/off current ratio of ∼10⁵, a large memory window over 60 V, good endurance, and a long retention time over 10³ seconds. In particular, the memory device can work as an artificial synapse to emulate basic synaptic functions and feature good linearity and symmetry in conductance change during long-term potentiation/depression processes. A simulated artificial neural network based on our synaptic device achieves a high accuracy of ∼88% in MNIST pattern recognition. Our work provides a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.