HfO<sub>2</sub>-based memristive synapses with asymmetrically extended p-n heterointerfaces for highly energy-efficient neuromorphic hardware.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41861000.
- Also identified by DOI 10.1126/sciadv.aec2324 and PMC identifier 13004029.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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<sub>2</sub> thin films for energy-efficient, resistive switching-based neuromorphic devices. We demonstrate interfacial memristors with ultralow switching currents (≤~10<sup>-8</sup> A), exceptional cycle-to-cycle and device-to-device uniformities, and retention >10<sup>5</sup> 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<sub>2</sub> and n-type TiO<sub>x</sub>N<sub>y</sub>, resulting in a fully depleted space-charge layer asymmetrically extended into Hf(Sr,Ti)O<sub>2</sub>, 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.