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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2412.18418 |
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| _version_ | 1866912169249996800 |
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| author | Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long |
| author_facet | Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long |
| contents | Neuromorphic computing, which seeks to replicate the brain's ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | All-electric mimicking synaptic plasticity based on the noncollinear antiferromagnetic device Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long Applied Physics Neuromorphic computing, which seeks to replicate the brain's ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems. |
| title | All-electric mimicking synaptic plasticity based on the noncollinear antiferromagnetic device |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2412.18418 |