(Invited) Development, Characterization, and Modeling of a TaOx ReRAM for a Neuromorphic Accelerator

Resistive random access memory (ReRAM), or memristors, may be capable of significantly improving the efficiency of neuromorphic computing, when used as a central component of an analog hardware accelerator. However, the significant electrical variation within a single device and between devices degrades the maximum efficiency and accuracy which can be achieved by a ReRAM-based neuromorphic accelerator. In this report, the electrical variability is characterized, with a particular focus on that which is due to fundamental, intrinsic factors. Analytical and ab initio models are presented which offer insight into the factors responsible for this variability.