Investigating Selectorless Property within Niobium Devices for Storage Applications

Resistive random-access memory (RRAM) crossbar arrays have shown significant promise as drivers of neuromorphic computing, in-memory computing, and high-density storage-class memory applications. However, leakage current through parasitic sneak paths is one of the dominant obstacles for large-scale commercial deployment of RRAM arrays. To overcome this issue without compromising on the structural simplicity, the use of inherent selectors native to switching is one of the most promising ways to reduce sneak path currents without sacrificing density associated with the simple two-electrode structure. In this study, niobium oxide (NbOx) was chosen as the resistive switching layer since it co-exhibits non-volatile memory and metal-insulator-transition selector behavior. Experimental results demonstrate abnormal phenomena in the reset process: a rapid decrease in current, followed by an increase when reset from the on state. The current conduction mechanism was examined through statistical analysis, and a conduction filament physical model was developed to explain the abnormal phenomenon. Under optimized operation conditions, non-linearity of similar to 500 and fast switching speeds of 30 ns set and 50 ns reset were obtained. The switching behaviors with the intrinsic selector property make the NbOx device an attractive candidate for future memory and in-memory computing applications.

Automated summary

Resistive random-access memory (RRAM) crossbar arrays have shown great potential for neuromorphic computing, in-memory computing, and high-density storage-class memory applications, but leakage currents through parasitic sneak paths remain a major obstacle. The use of inherent selectors native to switching, such as niobium oxide (NbOx), can help reduce sneak path currents without sacrificing structural simplicity. Experimental results demonstrate abnormal phenomena in the reset process, and the switching behaviors of NbOx devices make them attractive candidates for future memory and in-memory computing applications.