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Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator
The inherent nonlinear magnetization dynamics in spintronic devices make them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin transfer torque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188540/ https://www.ncbi.nlm.nih.gov/pubmed/37193725 http://dx.doi.org/10.1038/s41598-023-34849-7 |
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author | Mohan, John Rex Mathew, Arun Jacob Nishimura, Kazuma Feng, Ruoyan Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro |
author_facet | Mohan, John Rex Mathew, Arun Jacob Nishimura, Kazuma Feng, Ruoyan Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro |
author_sort | Mohan, John Rex |
collection | PubMed |
description | The inherent nonlinear magnetization dynamics in spintronic devices make them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin transfer torque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In this paper, with the help of micromagnetic simulations, we model and demonstrate that the magnetization dynamics of a single spin Hall oscillator can be nonlinearly transformed by harnessing input pulse streams and can be utilized for classification tasks. The spin Hall oscillator utilizes the microwave spectral characteristics of its magnetization dynamics for processing a binary data input. The spectral change due to the nonlinear magnetization dynamics assists in real-time feature extraction and classification of 4-binary digit input patterns. The performance was tested for the classification of the standard MNIST handwritten digit data set and achieved an accuracy of 83.1% in a simple linear regression model. Our results suggest that modulating time-driven input data can generate diverse magnetization dynamics in the spin Hall oscillator that can be suitable for temporal or sequential information processing. |
format | Online Article Text |
id | pubmed-10188540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101885402023-05-18 Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator Mohan, John Rex Mathew, Arun Jacob Nishimura, Kazuma Feng, Ruoyan Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro Sci Rep Article The inherent nonlinear magnetization dynamics in spintronic devices make them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin transfer torque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In this paper, with the help of micromagnetic simulations, we model and demonstrate that the magnetization dynamics of a single spin Hall oscillator can be nonlinearly transformed by harnessing input pulse streams and can be utilized for classification tasks. The spin Hall oscillator utilizes the microwave spectral characteristics of its magnetization dynamics for processing a binary data input. The spectral change due to the nonlinear magnetization dynamics assists in real-time feature extraction and classification of 4-binary digit input patterns. The performance was tested for the classification of the standard MNIST handwritten digit data set and achieved an accuracy of 83.1% in a simple linear regression model. Our results suggest that modulating time-driven input data can generate diverse magnetization dynamics in the spin Hall oscillator that can be suitable for temporal or sequential information processing. Nature Publishing Group UK 2023-05-16 /pmc/articles/PMC10188540/ /pubmed/37193725 http://dx.doi.org/10.1038/s41598-023-34849-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mohan, John Rex Mathew, Arun Jacob Nishimura, Kazuma Feng, Ruoyan Medwal, Rohit Gupta, Surbhi Rawat, Rajdeep Singh Fukuma, Yasuhiro Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title | Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title_full | Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title_fullStr | Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title_full_unstemmed | Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title_short | Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator |
title_sort | classification tasks using input driven nonlinear magnetization dynamics in spin hall oscillator |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188540/ https://www.ncbi.nlm.nih.gov/pubmed/37193725 http://dx.doi.org/10.1038/s41598-023-34849-7 |
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