Babak Bakhit, Xuejun Xie, Simon M. Fairclough, Atif Jan, Ingemar Persson, Giuliana Di Martino, Bonan Zhu, Caterina Ducati, Quanxi Jia, Bilge Yildiz, Andrew J. Flewitt, Judith MacManus-Driscoll
The escalating energy consumption of existing artificial intelligence hardware has become a serious global issue that demands immediate action. Neuromorphic computing offers promises to drastically reduce this footprint. Here, we introduce multicomponent p-type Hf(Sr,Ti)O 2 thin films for energy-efficient, resistive switching–based neuromorphic devices. We demonstrate interfacial memristors with ultralow switching currents (≤~10 −8 A), exceptional cycle-to-cycle and device-to-device uniformities, and retention >10 5 s. They reveal hundreds of ultralow conductance levels with a modulation range of >50 (without reaching any saturation) and reproducibly satisfy unsupervised learning rules. This performance originates from incorporating a self-assembled p-n heterointerface between p-type Hf(Sr,Ti)O 2 and n-type TiO x N y , resulting in a fully depleted space-charge layer asymmetrically extended into Hf(Sr,Ti)O 2 , a large built-in potential, and extremely low saturation current density under reverse bias. Ultralow conductance modulation is controlled by tuning p-n heterointerface’s energy-barrier height through electro-ionic charge migration. This materials-engineering strategy addresses energy consumption and variability in existing memristors, opening a pathway toward energy-efficient neuromorphic computing systems.