All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks
The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique...
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| Veröffentlicht in: | Advanced science Jg. 12; H. 22; S. e2417735 - n/a |
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John Wiley & Sons, Inc
01.06.2025
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| Abstract | The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique spintronic structures presented: the Néel orange‐peel effect, interlayer Dzyaloshinskii‐Moriya interaction (i‐DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle‐to‐cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11‐state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi‐state device in convolutional neural networks (CNNs) using post‐training quantization is implemented. Results indicate that per‐channel quantization, particularly with the min‐max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR‐10 dataset, achieving up to 81.51% and 81.12% in ResNet‐18—values that closely approach the baseline accuracy. This evaluation underscores the potential of field‐free spintronic synapses in neuromorphic architectures, offering an area‐efficient solution that integrates multi‐state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy‐efficient, high‐performance systems inspired by neural processes.
This study develops three all‐electrically controlled, field‐free spintronic synapse devices for neuromorphic computing. The tilted anisotropy device achieves an 11‐state memory with minimal cycle‐to‐cycle variation (2%), enabling high‐accuracy neural network quantization (81.51% in ResNet‐18). These findings position spintronic synapses as a promising solution for energy‐efficient AI hardware. |
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| AbstractList | Abstract The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique spintronic structures presented: the Néel orange‐peel effect, interlayer Dzyaloshinskii‐Moriya interaction (i‐DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle‐to‐cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11‐state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi‐state device in convolutional neural networks (CNNs) using post‐training quantization is implemented. Results indicate that per‐channel quantization, particularly with the min‐max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR‐10 dataset, achieving up to 81.51% and 81.12% in ResNet‐18—values that closely approach the baseline accuracy. This evaluation underscores the potential of field‐free spintronic synapses in neuromorphic architectures, offering an area‐efficient solution that integrates multi‐state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy‐efficient, high‐performance systems inspired by neural processes. The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique spintronic structures presented: the Néel orange‐peel effect, interlayer Dzyaloshinskii‐Moriya interaction (i‐DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle‐to‐cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11‐state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi‐state device in convolutional neural networks (CNNs) using post‐training quantization is implemented. Results indicate that per‐channel quantization, particularly with the min‐max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR‐10 dataset, achieving up to 81.51% and 81.12% in ResNet‐18—values that closely approach the baseline accuracy. This evaluation underscores the potential of field‐free spintronic synapses in neuromorphic architectures, offering an area‐efficient solution that integrates multi‐state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy‐efficient, high‐performance systems inspired by neural processes. The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all-electrically controlled, field-free spin synapse devices designed with unique spintronic structures presented: the Néel orange-peel effect, interlayer Dzyaloshinskii-Moriya interaction (i-DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle-to-cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11-state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi-state device in convolutional neural networks (CNNs) using post-training quantization is implemented. Results indicate that per-channel quantization, particularly with the min-max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR-10 dataset, achieving up to 81.51% and 81.12% in ResNet-18-values that closely approach the baseline accuracy. This evaluation underscores the potential of field-free spintronic synapses in neuromorphic architectures, offering an area-efficient solution that integrates multi-state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy-efficient, high-performance systems inspired by neural processes.The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all-electrically controlled, field-free spin synapse devices designed with unique spintronic structures presented: the Néel orange-peel effect, interlayer Dzyaloshinskii-Moriya interaction (i-DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle-to-cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11-state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi-state device in convolutional neural networks (CNNs) using post-training quantization is implemented. Results indicate that per-channel quantization, particularly with the min-max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR-10 dataset, achieving up to 81.51% and 81.12% in ResNet-18-values that closely approach the baseline accuracy. This evaluation underscores the potential of field-free spintronic synapses in neuromorphic architectures, offering an area-efficient solution that integrates multi-state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy-efficient, high-performance systems inspired by neural processes. The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique spintronic structures presented: the Néel orange‐peel effect, interlayer Dzyaloshinskii‐Moriya interaction (i‐DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle‐to‐cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11‐state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi‐state device in convolutional neural networks (CNNs) using post‐training quantization is implemented. Results indicate that per‐channel quantization, particularly with the min‐max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR‐10 dataset, achieving up to 81.51% and 81.12% in ResNet‐18—values that closely approach the baseline accuracy. This evaluation underscores the potential of field‐free spintronic synapses in neuromorphic architectures, offering an area‐efficient solution that integrates multi‐state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy‐efficient, high‐performance systems inspired by neural processes. This study develops three all‐electrically controlled, field‐free spintronic synapse devices for neuromorphic computing. The tilted anisotropy device achieves an 11‐state memory with minimal cycle‐to‐cycle variation (2%), enabling high‐accuracy neural network quantization (81.51% in ResNet‐18). These findings position spintronic synapses as a promising solution for energy‐efficient AI hardware. The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability. In this study, three types of all‐electrically controlled, field‐free spin synapse devices designed with unique spintronic structures presented: the Néel orange‐peel effect, interlayer Dzyaloshinskii‐Moriya interaction (i‐DMI), and tilted anisotropy. To systematically evaluate their neuromorphic potential, a benchmarking framework is introduced that characterizes cycle‐to‐cycle (CTC) variation, a critical factor for reliable synaptic weight updates. Among these designs, the tilted anisotropy device achieves an 11‐state memory with minimal CTC variation (2%), making it particularly suited for complex synaptic emulation. Through comprehensive benchmarking, this multi‐state device in convolutional neural networks (CNNs) using post‐training quantization is implemented. Results indicate that per‐channel quantization, particularly with the min‐max and mean squared error (MSE) observers, enhances classification accuracy on the CIFAR‐10 dataset, achieving up to 81.51% and 81.12% in ResNet‐18—values that closely approach the baseline accuracy. This evaluation underscores the potential of field‐free spintronic synapses in neuromorphic architectures, offering an area‐efficient solution that integrates multi‐state functionality with robust switching performance. The findings highlight the promise of these devices in advancing neuromorphic computing, contributing to energy‐efficient, high‐performance systems inspired by neural processes. This study develops three all‐electrically controlled, field‐free spintronic synapse devices for neuromorphic computing. The tilted anisotropy device achieves an 11‐state memory with minimal cycle‐to‐cycle variation (2%), enabling high‐accuracy neural network quantization (81.51% in ResNet‐18). These findings position spintronic synapses as a promising solution for energy‐efficient AI hardware. |
| Author | Yang, Chun Pai, Chi‐Feng Lin, Chun‐Yi Wang, Po‐Chuan Hsin, Tzu‐Chuan |
| AuthorAffiliation | 1 Department of Materials Science and Engineering National Taiwan University Taipei 10617 Taiwan |
| AuthorAffiliation_xml | – name: 1 Department of Materials Science and Engineering National Taiwan University Taipei 10617 Taiwan |
| Author_xml | – sequence: 1 givenname: Tzu‐Chuan orcidid: 0009-0007-5483-7163 surname: Hsin fullname: Hsin, Tzu‐Chuan organization: National Taiwan University – sequence: 2 givenname: Chun‐Yi orcidid: 0009-0005-7246-3535 surname: Lin fullname: Lin, Chun‐Yi organization: National Taiwan University – sequence: 3 givenname: Po‐Chuan orcidid: 0009-0002-4353-2974 surname: Wang fullname: Wang, Po‐Chuan organization: National Taiwan University – sequence: 4 givenname: Chun surname: Yang fullname: Yang, Chun organization: National Taiwan University – sequence: 5 givenname: Chi‐Feng orcidid: 0000-0001-6723-8302 surname: Pai fullname: Pai, Chi‐Feng email: cfpai@ntu.edu.tw organization: National Taiwan University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40285600$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1063_5_0282764 |
| Cites_doi | 10.1109/TED.2021.3111846 10.1002/aelm.202300885 10.1063/5.0226135 10.1002/adma.202103672 10.1038/nnano.2015.29 10.1109/JPROC.2020.3003007 10.1002/adfm.202404679 10.1063/5.0079400 10.1002/adma.202300107 10.1109/TNNLS.2024.3369969 10.1116/1.4936261 10.1103/PhysRevB.91.214434 10.1021/acsaelm.2c01488 10.1063/1.5042408 10.1038/s41598-018-26586-z 10.1103/PhysRevApplied.18.034046 10.1103/PhysRevB.93.144409 10.1063/5.0145873 10.1021/acsami.3c13775 10.1002/advs.202004645 10.1109/85.238389 10.1002/advs.202203006 10.1002/aelm.202300889 10.1007/s11432-020-3227-1 10.1038/s41427-023-00521-9 10.1109/TCAD.2018.2789723 10.1103/PhysRevB.96.104412 10.1109/ACCESS.2022.3196688 10.1109/TED.2025.3537592 10.1109/JETCAS.2019.2933148 10.1080/23746149.2016.1259585 10.1103/PhysRevLett.127.167202 10.1103/PhysRevApplied.19.024034 10.1038/s41467-024-48631-4 10.1063/5.0174903 10.1038/nnano.2016.29 10.1016/j.jmmm.2024.172726 10.1088/2634-4386/ac4a83 10.1088/1361-6528/ab967d 10.1038/s41563-019-0370-z 10.1063/5.0009482 10.1038/s41928-019-0360-9 10.1063/1.1315633 10.1063/1.4919867 10.1088/2634-4386/ac62db 10.1002/adma.201900636 10.1038/s41467-023-36728-1 10.1002/aelm.202300472 10.1063/5.0221776 10.1038/s41467-024-45670-9 10.1038/s41565-022-01213-1 10.1021/acsmaterialslett.3c01376 10.1103/PhysRevB.90.184427 10.1038/s41586-021-04196-6 10.1103/PhysRevB.100.104441 10.1038/s41427-021-00282-3 10.1063/1.4902443 10.1038/s41586-020-1942-4 10.1063/1.370376 10.1109/TED.2023.3327031 10.1038/nmat4566 10.1021/acsanm.2c04094 10.1021/acs.nanolett.4c01712 10.3390/electronics10091084 10.1002/aelm.201800782 10.1038/s41928-017-0002-z 10.1038/s41563-019-0386-4 |
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| Keywords | spin‐orbit torques field‐free switching perpendicular magnetic anisotropy neural network neuromorphic computing |
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| References | 2021; 69 2021; 68 2023; 35 2021; 64 2017; 2 2023; 5 2023; 6 2018; 124 2023; 9 2021; 127 2020; 128 2019; 18 1999; 85 2024; 34 2015; 106 2024 2024; 36 2016; 34 2025; 72 2022; 120 2018; 8 2020; 3 2021; 33 2018; 1 2020; 577 2024; 2 2015; 91 2024; 24 2022; 601 2018; 37 2023; 70 2021; 8 2019; 9 2023; 14 2023; 11 2014; 90 2019; 5 2019; 31 2011 2018; 107 2023; 19 2023; 122 2015; 10 2024; 10 2016; 93 2024; 12 2024; 15 2024; 16 2016; 15 2020; 108 2019; 100 2025; 614 2016; 11 1993; 15 2021; 13 2014; 105 2017; 96 2021; 10 2020; 31 2021 2000; 77 2022; 9 2019 2017 2016 2022; 10 2022; 2 2022; 17 2022; 18 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_68_1 Dieny B. (e_1_2_8_66_1) 2016 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_64_1 e_1_2_8_62_1 e_1_2_8_1_1 e_1_2_8_41_1 e_1_2_8_60_1 Nagel M. (e_1_2_8_44_1) 2021 e_1_2_8_17_1 Zhang Y. (e_1_2_8_43_1) 2021; 68 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_57_1 Aly M. M. S. (e_1_2_8_4_1) 2018; 107 e_1_2_8_70_1 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_76_1 e_1_2_8_51_1 e_1_2_8_74_1 e_1_2_8_30_1 e_1_2_8_72_1 e_1_2_8_29_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_69_1 e_1_2_8_2_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_67_1 e_1_2_8_23_1 e_1_2_8_65_1 e_1_2_8_63_1 e_1_2_8_40_1 e_1_2_8_61_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_58_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_77_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_75_1 e_1_2_8_52_1 e_1_2_8_73_1 e_1_2_8_50_1 e_1_2_8_71_1 |
| References_xml | – year: 2011 – volume: 6 start-page: 875 year: 2023 publication-title: ACS Appl. Nano Mater – volume: 91 year: 2015 publication-title: Phys. Rev. B – volume: 19 year: 2023 publication-title: Phys. Rev. Appl. – volume: 15 start-page: 1974 year: 2024 publication-title: Nat. Commun. – volume: 105 year: 2014 publication-title: Appl. Phys. Lett. – volume: 68 start-page: 1193 year: 2021 publication-title: IEEE Trans. Circ. Syst. I – volume: 2 year: 2022 publication-title: Neuromorph. Comput. Eng. – year: 2021 – volume: 577 start-page: 641 year: 2020 publication-title: Nature – year: 2024 – volume: 37 start-page: 3067 year: 2018 publication-title: IEEE Trans. on CAD – volume: 9 start-page: 570 year: 2019 publication-title: IEEE J. Emerg. Selected Topics Circ. Syst. – volume: 107 start-page: 19 year: 2018 publication-title: Proc. IEEE – volume: 2 start-page: 89 year: 2017 publication-title: Adv. Phys.: X – volume: 122 year: 2023 publication-title: Appl. Phys. Lett. – volume: 10 start-page: 1084 year: 2021 publication-title: Electronics – volume: 2 year: 2024 publication-title: APL Mach. Learn. – volume: 15 start-page: 4534 year: 2024 publication-title: Nat. Commun. – volume: 15 start-page: 27 year: 1993 publication-title: IEEE Ann. History Comput. – volume: 33 year: 2021 publication-title: Adv. Mater. – volume: 93 year: 2016 publication-title: Phys. Rev. B – volume: 100 year: 2019 publication-title: Phys. Rev. B – volume: 5 year: 2019 publication-title: Adv. Electron. Mater. – volume: 77 start-page: 2373 year: 2000 publication-title: P. Rice. Appl.Phys. Lett. – volume: 2 year: 2022 publication-title: Neur. Comp. Eng. – volume: 34 year: 2024 publication-title: Adv. Funct. Mater. – volume: 1 start-page: 52 year: 2018 publication-title: Nat. Electron – volume: 70 start-page: 6336 year: 2023 publication-title: IEEE Trans. Electron Devices – volume: 9 year: 2023 publication-title: Adv. Electron. Mater. – volume: 96 year: 2017 publication-title: Phys. Rev. B – year: 2019 – volume: 106 year: 2015 publication-title: Appl. Phys. Lett. – volume: 614 year: 2025 publication-title: J. Magn. Magn. Mater. – volume: 14 start-page: 1068 year: 2023 publication-title: Nat. Commun. – volume: 8 year: 2021 publication-title: Adv. Sci. – volume: 108 start-page: 2276 year: 2020 publication-title: Proc. IEEE – volume: 72 start-page: 1772 year: 2025 publication-title: IEEE Trans. Electron Devices – volume: 10 year: 2022 publication-title: J. Atulasimha. IEEE Access – volume: 64 year: 2021 publication-title: Sci. China Inf. Sci. – volume: 10 year: 2024 publication-title: Adv. Electron. Mater. – volume: 16 start-page: 1 year: 2024 publication-title: NPG Asia Mater – volume: 13 start-page: 11 year: 2021 publication-title: NPG Asia Mater – volume: 6 start-page: 400 year: 2023 publication-title: ACS Mater. Lett. – volume: 11 start-page: 621 year: 2016 publication-title: Nat. Nanotech – volume: 36 start-page: 4996 year: 2024 publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 18 year: 2022 publication-title: Phys. Rev. Appl. – volume: 601 start-page: 211 year: 2022 publication-title: Nature – volume: 35 year: 2023 publication-title: Adv. Mater. – volume: 90 year: 2014 publication-title: Phys. Rev. B – year: 2016 – volume: 85 start-page: 4466 year: 1999 publication-title: J. Appl. Phys. – volume: 17 start-page: 1065 year: 2022 publication-title: Nat. Nanotech – volume: 9 year: 2022 publication-title: Adv. Sci. – volume: 31 year: 2019 publication-title: Adv. Mater. – volume: 18 start-page: 703 year: 2019 publication-title: Nat. Mater. – year: 2021 publication-title: arXiv – volume: 34 year: 2016 publication-title: J. Vac. Sci. Technol. – volume: 18 start-page: 679 year: 2019 publication-title: Nat. Mater. – volume: 124 year: 2018 publication-title: J. Appl. Phys. – volume: 24 start-page: 7706 year: 2024 publication-title: Nano Lett. – volume: 127 year: 2021 publication-title: Phys. Rev. Lett. – volume: 5 start-page: 484 year: 2023 publication-title: ACS Appl. Electron. Mater. – volume: 11 year: 2023 publication-title: APL Mater. – volume: 12 year: 2024 publication-title: APL Mater. – volume: 15 start-page: 535 year: 2016 publication-title: Nat. Mater. – volume: 8 start-page: 8144 year: 2018 publication-title: Sci. Rep. – year: 2017 – volume: 16 start-page: 1054 year: 2024 publication-title: ACS Appl. Mater. Interfaces – volume: 31 year: 2020 publication-title: Nanotechnology – volume: 69 start-page: 1658 year: 2021 publication-title: IEEE Trans. Electron Devices – volume: 128 year: 2020 publication-title: J. Appl. Phys. – volume: 10 start-page: 191 year: 2015 publication-title: Nat. Nanotech – volume: 120 year: 2022 publication-title: Appl. Phys. Lett. – volume: 3 start-page: 360 year: 2020 publication-title: Nat. Electron – ident: e_1_2_8_70_1 – ident: e_1_2_8_22_1 doi: 10.1109/TED.2021.3111846 – ident: e_1_2_8_28_1 doi: 10.1002/aelm.202300885 – ident: e_1_2_8_60_1 doi: 10.1063/5.0226135 – ident: e_1_2_8_19_1 doi: 10.1002/adma.202103672 – ident: e_1_2_8_10_1 doi: 10.1038/nnano.2015.29 – ident: e_1_2_8_16_1 doi: 10.1109/JPROC.2020.3003007 – ident: e_1_2_8_35_1 doi: 10.1002/adfm.202404679 – ident: e_1_2_8_49_1 doi: 10.1063/5.0079400 – ident: e_1_2_8_15_1 doi: 10.1002/adma.202300107 – ident: e_1_2_8_39_1 doi: 10.1109/TNNLS.2024.3369969 – ident: e_1_2_8_55_1 doi: 10.1116/1.4936261 – ident: e_1_2_8_62_1 doi: 10.1103/PhysRevB.91.214434 – ident: e_1_2_8_13_1 – ident: e_1_2_8_36_1 doi: 10.1021/acsaelm.2c01488 – ident: e_1_2_8_14_1 doi: 10.1063/1.5042408 – ident: e_1_2_8_51_1 doi: 10.1038/s41598-018-26586-z – ident: e_1_2_8_58_1 doi: 10.1103/PhysRevApplied.18.034046 – ident: e_1_2_8_24_1 doi: 10.1103/PhysRevB.93.144409 – ident: e_1_2_8_65_1 doi: 10.1063/5.0145873 – ident: e_1_2_8_68_1 doi: 10.1021/acsami.3c13775 – ident: e_1_2_8_7_1 doi: 10.1002/advs.202004645 – ident: e_1_2_8_1_1 doi: 10.1109/85.238389 – ident: e_1_2_8_77_1 doi: 10.1002/advs.202203006 – ident: e_1_2_8_37_1 doi: 10.1002/aelm.202300889 – volume: 68 start-page: 1193 year: 2021 ident: e_1_2_8_43_1 publication-title: IEEE Trans. Circ. Syst. I – ident: e_1_2_8_12_1 – ident: e_1_2_8_3_1 doi: 10.1007/s11432-020-3227-1 – ident: e_1_2_8_63_1 doi: 10.1038/s41427-023-00521-9 – ident: e_1_2_8_9_1 – ident: e_1_2_8_29_1 doi: 10.1109/TCAD.2018.2789723 – ident: e_1_2_8_61_1 doi: 10.1103/PhysRevB.96.104412 – ident: e_1_2_8_74_1 doi: 10.1109/ACCESS.2022.3196688 – ident: e_1_2_8_38_1 doi: 10.1109/TED.2025.3537592 – ident: e_1_2_8_64_1 doi: 10.1109/JETCAS.2019.2933148 – ident: e_1_2_8_11_1 doi: 10.1080/23746149.2016.1259585 – ident: e_1_2_8_59_1 doi: 10.1103/PhysRevLett.127.167202 – ident: e_1_2_8_32_1 doi: 10.1103/PhysRevApplied.19.024034 – ident: e_1_2_8_72_1 doi: 10.1038/s41467-024-48631-4 – ident: e_1_2_8_46_1 doi: 10.1063/5.0174903 – ident: e_1_2_8_73_1 – ident: e_1_2_8_23_1 doi: 10.1038/nnano.2016.29 – ident: e_1_2_8_34_1 doi: 10.1016/j.jmmm.2024.172726 – ident: e_1_2_8_6_1 doi: 10.1088/2634-4386/ac4a83 – ident: e_1_2_8_75_1 doi: 10.1088/1361-6528/ab967d – ident: e_1_2_8_57_1 doi: 10.1038/s41563-019-0370-z – volume: 107 start-page: 19 year: 2018 ident: e_1_2_8_4_1 publication-title: Proc. IEEE – ident: e_1_2_8_8_1 doi: 10.1063/5.0009482 – ident: e_1_2_8_5_1 doi: 10.1038/s41928-019-0360-9 – year: 2021 ident: e_1_2_8_44_1 publication-title: arXiv – ident: e_1_2_8_47_1 doi: 10.1063/1.1315633 – ident: e_1_2_8_54_1 doi: 10.1063/1.4919867 – ident: e_1_2_8_40_1 doi: 10.1088/2634-4386/ac62db – ident: e_1_2_8_20_1 doi: 10.1002/adma.201900636 – ident: e_1_2_8_76_1 doi: 10.1038/s41467-023-36728-1 – ident: e_1_2_8_27_1 doi: 10.1002/aelm.202300472 – ident: e_1_2_8_53_1 doi: 10.1063/5.0221776 – ident: e_1_2_8_42_1 doi: 10.1038/s41467-024-45670-9 – ident: e_1_2_8_2_1 doi: 10.1038/s41565-022-01213-1 – ident: e_1_2_8_45_1 doi: 10.1021/acsmaterialslett.3c01376 – volume-title: Introduction to Magnetic Random‐Access Memory year: 2016 ident: e_1_2_8_66_1 – ident: e_1_2_8_52_1 doi: 10.1103/PhysRevB.90.184427 – ident: e_1_2_8_71_1 doi: 10.1038/s41586-021-04196-6 – ident: e_1_2_8_30_1 – ident: e_1_2_8_50_1 doi: 10.1103/PhysRevB.100.104441 – ident: e_1_2_8_25_1 doi: 10.1038/s41427-021-00282-3 – ident: e_1_2_8_18_1 doi: 10.1063/1.4902443 – ident: e_1_2_8_67_1 doi: 10.1038/s41586-020-1942-4 – ident: e_1_2_8_48_1 doi: 10.1063/1.370376 – ident: e_1_2_8_69_1 doi: 10.1109/TED.2023.3327031 – ident: e_1_2_8_21_1 doi: 10.1038/nmat4566 – ident: e_1_2_8_31_1 doi: 10.1021/acsanm.2c04094 – ident: e_1_2_8_26_1 doi: 10.1021/acs.nanolett.4c01712 – ident: e_1_2_8_33_1 doi: 10.3390/electronics10091084 – ident: e_1_2_8_17_1 doi: 10.1002/aelm.201800782 – ident: e_1_2_8_41_1 doi: 10.1038/s41928-017-0002-z – ident: e_1_2_8_56_1 doi: 10.1038/s41563-019-0386-4 |
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| Snippet | The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve... The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve... Abstract The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to... |
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| SubjectTerms | Anisotropy Artificial intelligence field‐free switching neural network Neural networks neuromorphic computing perpendicular magnetic anisotropy Phase transitions Software spin‐orbit torques Symmetry Synapses |
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| Title | All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks |
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