Unraveling the Interplay Between Memristive and Magnetoresistive Behaviors in LaCoO <sub>3</sub> /SrTiO <sub>3</sub> Superlattice‐Based Neural Synaptic Devices

Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO<sub>3</sub>/SrTiO<sub>3</sub>)<sub>n</sub>/LaCoO<sub>3</sub>/Nb:SrTiO<sub>3</sub> (Pt/(LCO/STO)<sub>n</sub>/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively. The results show that both memristive and magnetoresistive properties can coexist without any compromise in performance, and the values of R<sub>OFF</sub>/R<sub>ON</sub> and tunnel magnetoresistance (TMR) ratio are both improved by ≈1000% compared to a single-period heterostructure. Notably, the Pt/(LCO/STO)<sub>5</sub>/LCO/NSTO device demonstrates superior multilevel storage performance, characterized by extended endurance, reliable retention, high R<sub>OFF</sub>/R<sub>ON</sub> ratio, significant TMR ratio, and fundamental synaptic behaviors. Furthermore, density functional theory (DFT) is employed to calculate the changes in oxygen vacancies, affecting the overall energy bands and magnetic moments in the monolayer and multi-periodic structures. Simulations using the handwritten digit recognition classification achieve the highest accuracy of 94.38%. These attributes suggest that the devices hold considerable promise for application in data storage and neuromorphic computing, offering a platform for high-density neural circuits in intelligent electronic devices.

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Publication Details

Journal
Small Methods
Published
2024-12-24
DOI
https://doi.org/10.1002/smtd.202401259
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

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article

Unraveling the Interplay Between Memristive and Magnetoresistive Behaviors in LaCoO 3 /SrTiO 3 Superlattice‐Based Neural Synaptic Devices

Bingyang Ma, Xidong Duan, Jianling Yue, Zhu Tao et al.
5 citations
Small Methods
Advanced Memory and Neural Computing
1.04
article

Unraveling the Interplay Between Memristive and Magnetoresistive Behaviors in LaCoO 3 /SrTiO 3 Superlattice‐Based Neural Synaptic Devices

Bingyang Ma, Xidong Duan, Jianling Yue, Zhu Tao, Hailong Hu, Yu Liu, Xiaozhong Huang, Lanzhi Wang, Ze Wang, Zhengwei Zhang, Hailong Shang, Zeou Yang
article en
5 citations

Abstract

Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO<sub>3</sub>/SrTiO<sub>3</sub>)<sub>n</sub>/LaCoO<sub>3</sub>/Nb:SrTiO<sub>3</sub> (Pt/(LCO/STO)<sub>n</sub>/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively. The results show that both memristive and magnetoresistive properties can coexist without any compromise in performance, and the values of R<sub>OFF</sub>/R<sub>ON</sub> and tunnel magnetoresistance (TMR) ratio are both improved by ≈1000% compared to a single-period heterostructure. Notably, the Pt/(LCO/STO)<sub>5</sub>/LCO/NSTO device demonstrates superior multilevel storage performance, characterized by extended endurance, reliable retention, high R<sub>OFF</sub>/R<sub>ON</sub> ratio, significant TMR ratio, and fundamental synaptic behaviors. Furthermore, density functional theory (DFT) is employed to calculate the changes in oxygen vacancies, affecting the overall energy bands and magnetic moments in the monolayer and multi-periodic structures. Simulations using the handwritten digit recognition classification achieve the highest accuracy of 94.38%. These attributes suggest that the devices hold considerable promise for application in data storage and neuromorphic computing, offering a platform for high-density neural circuits in intelligent electronic devices.

Small MethodsVol. 9(5)
Central South University (CN), Hunan University (CN), Chinese Academy of Sciences (CN), Songshan Lake Materials Laboratory (CN), Shanghai Dianji University (CN), Institute of Physics (CN)
Natural Science Foundation of Hunan Province, State Key Laboratory of High Performance Ceramics and Superfine Microstructure
Affordable and clean energy
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
1.04
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