LAUSR

Volatility in metal-oxide resistive random access memory (RRAM) families has mostly been treated as an unwanted side-effect, although recently there are trends to interpret such behavior as an additional technological feature. To date, the field has seen early demonstrations of possible applications that harness volatility. Moreover, some work has been conducted to understand both the mechanisms responsible for this behavior. In the context of modeling RRAM volatility, we still lack a comprehensive model that could allow simulations in a larger scale. In an attempt to fill this gap, this work presents a modeling framework that can account for RRAM relaxation characteristics. Specifically, we show how volatility can be simulated to significant accuracy when the resistive state (RS) of a device as well as the stimulus protocol in use are well-defined. Importantly, our approach is solely data-driven and decoupled from previous physical modeling studies on volatility. Our results work for both stimulation polarities and are consistent for a number of $TiO_{x}$ devices in use. Moreover, the mathematical relations that unfold via modeling volatility provide further intuition on the effect that invasive protocols can have on this technology. This modeling solution enables more advanced studying of memristive technologies in one hand, as well as more intricate designs of larger systems that can account for transient RRAM changes over time.