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Resistive random access memories are promising for non-volatile memory andbrain-inspired computing applications. High variability and low yield of thesedevices are key drawbacks hindering reliable training of physical neuralnetworks. In this study, we show that doping an oxide electrolyte, Al2O3, withelectronegative metals makes resistive switching significantly morereproducible, surpassing the reproducibility requirements for obtainingreliable hardware neuromorphic circuits. The underlying mechanism is the easeof creating oxygen vacancies in the vicinity of electronegative dopants, due tothe capture of the associated electrons by dopant mid-gap states, and theweakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bindsignificantly to the dopant, thereby serving as preferential sites and buildingblocks in the formation of conducting paths. We validate this theoryexperimentally by implanting multiple dopants over a range ofelectronegativities, and find superior repeatability and yield with highlyelectronegative metals, Au, Pt and Pd. These devices also exhibit a gradual SETtransition, enabling multibit switching that is desirable for analog computing.

Electronegative metal dopants improve switching consistency in Al2O3 resistive switching devices