Controlling Native Oxidation of HfS<sub>2</sub> for 2D Materials Based Flash Memory and Artificial Synapse
Tengyu Jin, Yupeng Zheng, Jing Gao, Yanan Wang, Enlong Li, Huipeng Chen, Xuan Pan, Ming Lin, Wei Chen
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, HfOx, which shows good insulating property and clean interface with HfS2. We then incorporate the HfOx/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.