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Protonic nickelate device networks for spatiotemporal neuromorphic computing.

Yue Zhou¹, Shaan Shah¹, Tamal Dey²

¹Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA.

Nature Nanotechnology

|March 10, 2026

Summary

This study introduces a novel neuromorphic computing platform using perovskite nickelates. This integrated system efficiently combines processing and memory for advanced AI hardware, enabling real-time pattern recognition.

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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Area of Science:

Materials Science and Engineering
Neuromorphic Computing
Artificial Intelligence Hardware

Background:

Biological neural computation relies on complex spatiotemporal dynamics, which are difficult to replicate in current hardware.
Existing neuromorphic devices often emulate isolated neuron or synapse functions, limiting their computational richness.
The integration of nonlinear spatiotemporal processing and memory in a single material system remains a significant challenge.

Purpose of the Study:

To develop an integrated neuromorphic computing platform capable of both nonlinear spatiotemporal processing and programmable memory.
To utilize a single perovskite nickelate material system for realizing these advanced functionalities.
To demonstrate the platform's capability for real-time pattern recognition and classification tasks.

Main Methods:

Engineered symmetric and asymmetric hydrogenated NdNiO3 junction devices on a single wafer.
Leveraged proton-mediated transient dynamics for ultrafast operations and stable multilevel resistance states for memory.
Developed networks of these junctions to exhibit emergent spatial interactions and temporal memory.

Main Results:

Achieved nanosecond-scale operation with low energy consumption (~0.2 nJ per input) through proton redistribution and short-term temporal memory.
Integrated feature transformation and linear classification within the same material system using reconfigurable long-term weights.
Demonstrated high accuracy in spoken digit classification and early seizure detection, outperforming existing architectures.

Conclusions:

Protonic nickelates offer a compact, energy-efficient, and CMOS-compatible platform for integrated processing and memory.
The developed neuromorphic platform shows significant potential for scalable intelligent hardware.
This approach advances the realization of sophisticated computation in artificial systems.