Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices
Abstract Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin‐orbit‐torque‐induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state‐of‐the‐art artificial synapses based on ferrimagnet suffer from poor ana...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-04-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400591 |
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| _version_ | 1850199256206409728 |
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| author | Junwei Zeng Binxuan Zhao Yakun Liu Teng Xu Wanjun Jiang Liang Fang Jiahao Liu |
| author_facet | Junwei Zeng Binxuan Zhao Yakun Liu Teng Xu Wanjun Jiang Liang Fang Jiahao Liu |
| author_sort | Junwei Zeng |
| collection | DOAJ |
| description | Abstract Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin‐orbit‐torque‐induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state‐of‐the‐art artificial synapses based on ferrimagnet suffer from poor analog switching linearity, which serves as a bottleneck for achieving complex tasks with high accuracy in neuromorphic computing. Here, an artificial synapse is reported with high‐weight update linearity in a compensated ferrimagnetic crossbar device. In particular, the linear weight update of the synapses is enhanced by engineering the current density distribution. Using experimentally derived device parameters, handwritten digit recognition can be achieved with an accuracy of over 95% in a three‐layer fully connected artificial neural network. The work provides a universal method to improve the synaptic linearity, which also paves the way for applying the spin‐orbit device in neuromorphic computing. |
| format | Article |
| id | doaj-art-3ff9e162a7e84232aa97ccf6f475ed62 |
| institution | OA Journals |
| issn | 2199-160X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-3ff9e162a7e84232aa97ccf6f475ed622025-08-20T02:12:40ZengWiley-VCHAdvanced Electronic Materials2199-160X2025-04-01115n/an/a10.1002/aelm.202400591Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic DevicesJunwei Zeng0Binxuan Zhao1Yakun Liu2Teng Xu3Wanjun Jiang4Liang Fang5Jiahao Liu6College of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano‐Optoelectronic Information Materials and Devices National University of Defense Technology Changsha Hunan 410073 ChinaAnhui Province Key Laboratory of Low‐Energy Quantum Materials and Devices High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei 230031 ChinaArtificial Intelligence Research Center Defense Innovation Institute Beijing 100072 ChinaAnhui Province Key Laboratory of Low‐Energy Quantum Materials and Devices High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei 230031 ChinaState Key Laboratory of Low‐Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 ChinaInstitute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 ChinaCollege of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano‐Optoelectronic Information Materials and Devices National University of Defense Technology Changsha Hunan 410073 ChinaAbstract Ferrimagnetic materials with antiparallel exchange coupling, exhibit spin‐orbit‐torque‐induced dynamics, offering an emerging platform for realizing neuromorphic devices, such as artificial synapses. However, the state‐of‐the‐art artificial synapses based on ferrimagnet suffer from poor analog switching linearity, which serves as a bottleneck for achieving complex tasks with high accuracy in neuromorphic computing. Here, an artificial synapse is reported with high‐weight update linearity in a compensated ferrimagnetic crossbar device. In particular, the linear weight update of the synapses is enhanced by engineering the current density distribution. Using experimentally derived device parameters, handwritten digit recognition can be achieved with an accuracy of over 95% in a three‐layer fully connected artificial neural network. The work provides a universal method to improve the synaptic linearity, which also paves the way for applying the spin‐orbit device in neuromorphic computing.https://doi.org/10.1002/aelm.202400591artificial synapsesferrimagnetneuromorphic computingspin‐orbit torquespintronics |
| spellingShingle | Junwei Zeng Binxuan Zhao Yakun Liu Teng Xu Wanjun Jiang Liang Fang Jiahao Liu Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices Advanced Electronic Materials artificial synapses ferrimagnet neuromorphic computing spin‐orbit torque spintronics |
| title | Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices |
| title_full | Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices |
| title_fullStr | Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices |
| title_full_unstemmed | Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices |
| title_short | Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices |
| title_sort | linear weight update synaptic responses in ferrimagnetic neuromorphic devices |
| topic | artificial synapses ferrimagnet neuromorphic computing spin‐orbit torque spintronics |
| url | https://doi.org/10.1002/aelm.202400591 |
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