SUMMARY

Memristors are considered as one of the promising candidates for next-generation computation and storage systems. Neuromorphic computing architectures based on memristors were demonstrated to be very efficient in neural network training. Memristors made of transition-metal-oxides, like HfOx, achieve the resistive switching by formation and rupture of conductive filaments. The formation of filaments is driven by diffusion, drift and electrophoresis of oxygen vacancies, which are affected by electric field, Joule heating, etc. When bias is applied in the presence of conductive filament, high current and resulting temperature rise may result in structural change driven by thermal gradients and electric field. The physics of memristor is not completely understood, and there are many challenges for their employment in commercial applications. Accurate computational modeling of memristor operation requires correct material properties such as thermal conductivity and diffusion coefficient, many of which are not available. A better understanding of the material properties and high fidelity modeling techniques will push this field to a new stage. This research first investigates the structural and diffusion properties of amorphous memristor material through molecular dynamics simulation. Next, a frequency domain thermoreflectance (FDTR) set-up is developed to measure the thermal properties of materials used in memristor devices. A deep learning based data analysis pipeline is built to improve the measurement confidence. The transient thermal profiles of memristor have been measured by a thermoreflectance system to better understand the device operation which will provide guidelines for the design of memristors for various electronic applications.