Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications

Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications Aijaz H. Lone, Xuecui Zou, Kishan K. Mishra, Venkatesh Singaravelu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3839002/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2023 Read the published version in Nanoscale → Version 1 posted You are reading this latest preprint version Abstract Spintronics has made substantial progress due to its applications in energy-efficient memory, logic, and unconventional computing paradigms. Multilayer ferromagnetic thin films are extensively studied for understanding the domain wall and skyrmion dynamics. However, most of these studies are confined to the materials and domain wall/skyrmion physics. This paper presents the experimental and micromagnetic realization of a multilayer ferromagnetic spintronic device for neuromorphic computing applications. The device exhibits multilevel resistance states, and the number of resistance states increases with a lower temperature. This is supported by the multilevel magnetization behavior observed in the micromagnetic simulations. Furthermore, we also do experiments and simulations to realize current-driven multi-step resistance states. Using the multi-level resistance states of the device, we propose its applications as a synaptic device in hardware neural networks and study the linearity performance of the synaptic devices. The neural network based on these devices is trained and tested on the MNIST dataset using a supervised learning algorithm. When integrated as a weight into a 3-layer, fully connected neural network, these devices achieve above 90% recognition accuracy on the MNIST dataset. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Physical sciences/Physics/Applied physics Spintronics Domain wall devices multi-state memory Micromagnetics Synaptic devices and Neuromorphic computing Full Text Additional Declarations (Not answered) Supplementary material and Supplementary video are not available with this version. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2023 Read the published version in Nanoscale → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3839002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267824849,"identity":"7be8dd7b-c6c1-428a-85b7-22ed44492317","order_by":0,"name":"Aijaz H. 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Multilayer ferromagnetic\r\nthin films are extensively studied for understanding the domain wall and skyrmion\r\ndynamics. However, most of these studies are confined to the materials and domain\r\nwall/skyrmion physics. This paper presents the experimental and micromagnetic realization\r\nof a multilayer ferromagnetic spintronic device for neuromorphic computing\r\napplications. The device exhibits multilevel resistance states, and the number of resistance\r\nstates increases with a lower temperature. This is supported by the multilevel\r\nmagnetization behavior observed in the micromagnetic simulations. Furthermore, we\r\nalso do experiments and simulations to realize current-driven multi-step resistance\r\nstates. Using the multi-level resistance states of the device, we propose its applications\r\nas a synaptic device in hardware neural networks and study the linearity performance\r\nof the synaptic devices. The neural network based on these devices is trained and\r\ntested on the MNIST dataset using a supervised learning algorithm. When integrated\r\nas a weight into a 3-layer, fully connected neural network, these devices achieve above\r\n90% recognition accuracy on the MNIST dataset.","manuscriptTitle":"Multilayer Ferromagnetic Spintronic Devices for\nNeuromorphic Computing Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 18:55:24","doi":"10.21203/rs.3.rs-3839002/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e06b62f-8873-4def-ab9a-7eb00c98b553","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28220793,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":28220794,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices"},{"id":28220795,"name":"Physical sciences/Physics/Applied physics"}],"tags":[],"updatedAt":"2024-06-04T00:33:43+00:00","versionOfRecord":{"articleIdentity":"rs-3839002","link":"https://doi.org/10.1039/D4NR01003E","journal":{"identity":"nanoscale","isVorOnly":true,"title":"Nanoscale"},"publishedOn":"2024-01-01 00:33:43","publishedOnDateReadable":"January 1st, 2024"},"versionCreatedAt":"2024-01-22 18:55:24","video":"","vorDoi":"10.1039/D4NR01003E","vorDoiUrl":"https://doi.org/10.1039/D4NR01003E","workflowStages":[]},"version":"v1","identity":"rs-3839002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3839002","identity":"rs-3839002","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}