  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.
