A magneto-ionic synapse for reservoir computing
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915065343508480 |
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| author | Das, Sreeveni Mansell, Rhodri Flajšman, Lukáš Syskaki, Maria-Andromachi Langer, Jürgen van Dijken, Sebastiaan |
| author_facet | Das, Sreeveni Mansell, Rhodri Flajšman, Lukáš Syskaki, Maria-Andromachi Langer, Jürgen van Dijken, Sebastiaan |
| contents | Neuromorphic computing aims to revolutionize large-scale data processing by developing efficient methods and devices inspired by neural networks. Among these, the control of magnetism through ion migration has emerged as a promising approach due to the inherent memory and nonlinearity of ionically conducting and magnetic materials. In this work, we present a lithium-ion-based magneto-ionic device that uses applied voltages to control the magnetic domain state of a perpendicularly magnetized ferromagnetic layer. This behavior emulates the analog and non-volatile properties of biological synapses and enables the creation of a simple reservoir computing system. To illustrate its capabilities, the device is used in a waveform classification task, where the voltage amplitude range and magnetic bias field are tuned to optimize the recognition accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11297 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A magneto-ionic synapse for reservoir computing Das, Sreeveni Mansell, Rhodri Flajšman, Lukáš Syskaki, Maria-Andromachi Langer, Jürgen van Dijken, Sebastiaan Applied Physics Neuromorphic computing aims to revolutionize large-scale data processing by developing efficient methods and devices inspired by neural networks. Among these, the control of magnetism through ion migration has emerged as a promising approach due to the inherent memory and nonlinearity of ionically conducting and magnetic materials. In this work, we present a lithium-ion-based magneto-ionic device that uses applied voltages to control the magnetic domain state of a perpendicularly magnetized ferromagnetic layer. This behavior emulates the analog and non-volatile properties of biological synapses and enables the creation of a simple reservoir computing system. To illustrate its capabilities, the device is used in a waveform classification task, where the voltage amplitude range and magnetic bias field are tuned to optimize the recognition accuracy. |
| title | A magneto-ionic synapse for reservoir computing |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2412.11297 |