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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.19304 |
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| _version_ | 1866929693224075264 |
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| author | Huang, Weichuan Fang, Yue-Wen Yin, Yuewei Tian, Bobo Zhao, Wenbo Hou, Chuangming Ma, Chao Li, Qi Tsymbal, Evgeny Y. Duan, Chun-Gang Li, Xiaoguang |
| author_facet | Huang, Weichuan Fang, Yue-Wen Yin, Yuewei Tian, Bobo Zhao, Wenbo Hou, Chuangming Ma, Chao Li, Qi Tsymbal, Evgeny Y. Duan, Chun-Gang Li, Xiaoguang |
| contents | Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through investigating the memristor behaviors in a La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 multiferroic tunnel junction, it was found that the ferroelectric domain dynamics characteristics are influenced by the relative magnetization alignment of the electrodes, and the interfacial spin polarization is manipulated continuously by ferroelectric domain reversal, enriching our understanding of the magnetoelectric coupling fundamentally. This creates a functionality that not only the resistance of the memristor but also the synaptic plasticity form can be further manipulated, as demonstrated by the spike-timing-dependent plasticity investigations. Density functional theory calculations are carried out to describe the obtained magnetoelectric coupling, which is probably related to the Mn-Ti intermixing at the interfaces. The multiple and controllable plasticity characteristic in a single artificial synapse, to resemble the synaptic morphological alteration property in a biological synapse, will be conducive to the development of artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solid-state Synapse Based on Magnetoelectrically Coupled Memristor Huang, Weichuan Fang, Yue-Wen Yin, Yuewei Tian, Bobo Zhao, Wenbo Hou, Chuangming Ma, Chao Li, Qi Tsymbal, Evgeny Y. Duan, Chun-Gang Li, Xiaoguang Materials Science Strongly Correlated Electrons Applied Physics Brain-inspired computing architectures attempt to emulate the computations performed in the neurons and the synapses in human brain. Memristors with continuously tunable resistances are ideal building blocks for artificial synapses. Through investigating the memristor behaviors in a La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 multiferroic tunnel junction, it was found that the ferroelectric domain dynamics characteristics are influenced by the relative magnetization alignment of the electrodes, and the interfacial spin polarization is manipulated continuously by ferroelectric domain reversal, enriching our understanding of the magnetoelectric coupling fundamentally. This creates a functionality that not only the resistance of the memristor but also the synaptic plasticity form can be further manipulated, as demonstrated by the spike-timing-dependent plasticity investigations. Density functional theory calculations are carried out to describe the obtained magnetoelectric coupling, which is probably related to the Mn-Ti intermixing at the interfaces. The multiple and controllable plasticity characteristic in a single artificial synapse, to resemble the synaptic morphological alteration property in a biological synapse, will be conducive to the development of artificial intelligence. |
| title | Solid-state Synapse Based on Magnetoelectrically Coupled Memristor |
| topic | Materials Science Strongly Correlated Electrons Applied Physics |
| url | https://arxiv.org/abs/2501.19304 |