HfO2-based memristive synapses with asymmetrically extended p-n heterointerfaces for highly energy-efficient neuromorphic hardware

TL;DR

This paper introduces a new type of energy-efficient artificial synapse using HfO2-based materials for neuromorphic computing.

Contribution

A novel p-n heterointerface design in memristive devices enables ultralow energy consumption and high stability for neuromorphic hardware.

Findings

Memristors show ultralow switching currents (≤~10−8 A) and high retention (>105 s).

Devices exhibit hundreds of low conductance levels with a modulation range of >50.

The p-n heterointerface design reduces energy consumption and variability in memristors.

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)O2 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 >105 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)O2 and n-type TiOxNy, resulting in a fully depleted space-charge layer asymmetrically extended into Hf(Sr,Ti)O2, 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.

Memristive p-n heterointerfaces enable ultralow-energy, highly stable artificial synapses for future neuromorphic hardware.

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