Aim 1 examines the physics of adaptive oxide synaptic devices through a compact model that effectively emulates experimental findings and studies the effect of titanium doping on the resistance change mechanisms of these devices. 

Aim 2 focuses on improving the performance of adaptive oxide synaptic devices. Abrupt resistance changes during operation are addressed by a thin barrier layer that controls oxygen ion movement. Furthermore, MAX phase materials are utilized in these devices for enhanced endurance, reduced off-state current, higher on-off ratio, and multi-state capability.

Aim 3 extends the application of these findings to optimize system-level performance in an analog brain-inspired chip for deep learning. It led to the development of a novel electrical biasing technique, ReSta, which effectively restores the performance of analog AI hardware post-fatigue and the demonstration of transfer learning on analog AI hardware.

Overall, this dissertation aims to advance brain-inspired computing, offering new insights and methodologies at the material, device, and system levels, thus paving the way for more robust, efficient, and sustainable AI.