Monolithic Integration of Sensing, Computing, and Storage in an Anomalous Hall Effect-Based Neuromorphic Device

Conventional neuromorphic computing is hindered by the physical separation of the sensing, memory, and processing units. This work addresses the critical challenge of intrinsically integrating these functions within a monolithic device. We present a novel Ag/NiO/NiCo<sub>2</sub>O<sub>4</sub> heterostructure that integrates magnetic sensing, memristor-based storage, and analog computation together. This device exploits the anomalous Hall effect in the NiCo<sub>2</sub>O<sub>4</sub> layer, which exhibits perpendicular magnetic anisotropy, to concurrently enable magnetic field transduction (sensing) and intrinsic sigmoid activation function (computation). Concurrently, a nickel oxide layer serves as a nonvolatile memristor, achieving reconfigurable storage by further modulating the square anomalous Hall loop of the NiCo<sub>2</sub>O<sub>4</sub> layer (storage). Crucially, the memory state of the nickel oxide layer electrically reconfigures the anomalous Hall effect response of the NiCo<sub>2</sub>O<sub>4</sub> layer, which is achieved through interfacial modulation via oxygen vacancy migration. Experimentally, programming the nickel oxide memristor with low-voltage excitation (±2 V) controllably and nonvolatily modulates the sigmoidal activation threshold. Upon excitation, the device can be programmatically switched between high-sensitivity (3 V/(A·T)) and wide-dynamic-range (±200 mT) modes while maintaining native compatibility with analog computing architectures. This monolithic integration establishes a viable foundation for energy-efficient neuromorphic sensors and multifunctional autonomous systems.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-12-23
DOI
https://doi.org/10.1021/acsami.5c20352
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Monolithic Integration of Sensing, Computing, and Storage in an Anomalous Hall Effect-Based Neuromorphic Device

Lvkang Shen, Mei Liu, Yue Wang, Tianyu Liu et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Monolithic Integration of Sensing, Computing, and Storage in an Anomalous Hall Effect-Based Neuromorphic Device

Lvkang Shen, Mei Liu, Yue Wang, Tianyu Liu, Sitong An, Qiuyang Han
article en

Abstract

Conventional neuromorphic computing is hindered by the physical separation of the sensing, memory, and processing units. This work addresses the critical challenge of intrinsically integrating these functions within a monolithic device. We present a novel Ag/NiO/NiCo<sub>2</sub>O<sub>4</sub> heterostructure that integrates magnetic sensing, memristor-based storage, and analog computation together. This device exploits the anomalous Hall effect in the NiCo<sub>2</sub>O<sub>4</sub> layer, which exhibits perpendicular magnetic anisotropy, to concurrently enable magnetic field transduction (sensing) and intrinsic sigmoid activation function (computation). Concurrently, a nickel oxide layer serves as a nonvolatile memristor, achieving reconfigurable storage by further modulating the square anomalous Hall loop of the NiCo<sub>2</sub>O<sub>4</sub> layer (storage). Crucially, the memory state of the nickel oxide layer electrically reconfigures the anomalous Hall effect response of the NiCo<sub>2</sub>O<sub>4</sub> layer, which is achieved through interfacial modulation via oxygen vacancy migration. Experimentally, programming the nickel oxide memristor with low-voltage excitation (±2 V) controllably and nonvolatily modulates the sigmoidal activation threshold. Upon excitation, the device can be programmatically switched between high-sensitivity (3 V/(A·T)) and wide-dynamic-range (±200 mT) modes while maintaining native compatibility with analog computing architectures. This monolithic integration establishes a viable foundation for energy-efficient neuromorphic sensors and multifunctional autonomous systems.

ACS Applied Materials & InterfacesVol. 18(1)
Xi'an Jiaotong University (CN)
Affordable and clean energy
Openalex Percentile: Top 43%
Advanced Memory and Neural Computing
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