Electronegative metal dopants improve switching consistency in Al2O3 resistive switching devices.

Resistive random access memories are promising for non-volatile memory and brain-inspired computing applications. High variability and low yield of these devices are key drawbacks hindering reliable training of physical neural networks. In this study, we show that doping an oxide electrolyte, Al2O3, with electronegative metals makes resistive switching significantly more reproducible, surpassing the reproducibility requirements for obtaining reliable hardware neuromorphic circuits. The underlying mechanism is the ease of creating oxygen vacancies in the vicinity of electronegative dopants, due to the capture of the associated electrons by dopant mid-gap states, and the weakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind significantly to the dopant, thereby serving as preferential sites and building blocks in the formation of conducting paths. We validate this theory experimentally by implanting multiple dopants over a range of electronegativities, and find superior repeatability and yield with highly electronegative metals, Au, Pt and Pd. These devices also exhibit a gradual SET transition, enabling multibit switching that is desirable for analog computing.