FURI | Spring 2023
Characterization of Emerging Devices that will Integrate Memristor and Transistor Functions for Brain-Inspired Computing
The implementation of neural network models in hardware has been widely researched in the field of neuromorphic computing. Drifting away from the Von Neumann architecture, neuromorphic hardware allows for higher computational efficiency and performance. In this project, an investigation of neuromorphic devices that integrate the functionality of two devices by stacks of layers of 2D materials will take place. This device combines a molybdenum disulfide (MoS2) field-effect-transistor (FET) and a hexagonal boron nitride (hBN) memristor to enable better selectivity, programmability, and may also provide gate-tunable synaptic behavior to enable innovative brain-inspired computing architectures. Electrical characterization of the individual components will be conducted to understand the performance of each device function.
Hometown: Fremont, California, United States
Graduation date: Spring 2023