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.
Authors
- Bingyang Ma (ORCID: https://orcid.org/0000-0002-9306-135X)
- Xidong Duan (ORCID: https://orcid.org/0000-0002-4951-901X)
- Jianling Yue (ORCID: https://orcid.org/0000-0002-1089-6934)
- Zhu Tao (ORCID: https://orcid.org/0000-0002-8313-1430)
- Hailong Hu (ORCID: https://orcid.org/0000-0001-5299-2812)
- Yu Liu (ORCID: https://orcid.org/0000-0001-6087-8374)
- Xiaozhong Huang (ORCID: https://orcid.org/0000-0002-2127-0451)
- Lanzhi Wang (ORCID: https://orcid.org/0000-0003-3666-8208)
- Ze Wang (ORCID: https://orcid.org/0000-0002-5279-4135)
- Zhengwei Zhang (ORCID: https://orcid.org/0000-0001-8314-5341)
- Hailong Shang
- Zeou Yang
Institutions
- 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)
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
Funders
- Natural Science Foundation of Hunan Province
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure