Advancing Nano sheet Transistor Modeling with Gradient based Boosting Machines

Advancing Nano sheet Transistor Modeling with Gradient based Boosting Machines | 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 Advancing Nano sheet Transistor Modeling with Gradient based Boosting Machines kiran mandrumaka, Francis Harish Reddy Gade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8923546/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the realm of semiconductor-based device compacts modeling, machine Learning-Driven (ML) standing as a pivotal force, promising a significant strides in predictive analysis capabilities. ML-based compact modeling offered a distinctive advantage in capturing of intricate relationships and patterns within extensively datasets. This paper introduced a pioneering designed approach centered on Gradient based Boosting Machines (GBM) to formulated a rapid and precisely compact model (CM). This framework presents a combustive yet computationally efficient methodology, showcases an emergent dynamically varying behavioral activity. The study underscored the viability of ML-driven compact models in emulating to the performance of conventional models for nano sized devices, particularly gate-all-around (GAA) nano sized sheet (NS) devices. The research extensively investigated the device characteristics of GAA NS devices, focused on various sources of process variability. The Key geometrical parameters like channel length, nano sized sheet width, and nano sized sheet thickness also served as input features for the GBM model. With Through dynamically learning mechanisms and iteratively weight updating cycle, the network achieves an accurate convergence, effectively predicting of the electrical characteristics of NS devices with an error rate of less than 1%. Moreover, the proposed model is validated through simulations of digital based circuit designs, including of NAND and NOR logic gates. This abstract highlighted the transformative potential of Gradient based Boosting Machines in advancing in the modeling and simulation of Gate All-Around Nano sized sheet Transistors, underlining of their efficacy by driving innovation within the semiconductor industry. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Materials science/Nanoscale materials/Electronic properties and materials Gate-All-Around modelling Nano sized sheet Transistors Transistor Device Modeling Circuit Modeling process Gradient based Boosting Machines GBM Full Text Additional Declarations There is no conflict of interest Cite Share Download PDF Status: Posted 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. 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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-8923546","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595160190,"identity":"d15d7db5-8404-4bf7-83e7-1a4df765dbee","order_by":0,"name":"kiran mandrumaka","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5728-1152","institution":"Anurag University, Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"kiran","middleName":"","lastName":"mandrumaka","suffix":""},{"id":595160191,"identity":"e35dc92e-9b65-4b9a-8aed-a504a48a7902","order_by":1,"name":"Francis Harish Reddy Gade","email":"","orcid":"","institution":"Anurag University","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Harish Reddy","lastName":"Gade","suffix":""}],"badges":[],"createdAt":"2026-02-20 07:59:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8923546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8923546/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397669,"identity":"2fe571dc-52a4-4f3f-861d-324107629c69","added_by":"auto","created_at":"2026-03-11 11:54:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1189084,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Kiran.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8923546/v1_covered_2bf3ba40-48f9-42de-927d-d7ad484d0631.pdf"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Advancing Nano sheet Transistor Modeling with Gradient based \r\nBoosting Machines","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Gate-All-Around modelling, Nano sized sheet Transistors, Transistor Device Modeling, Circuit Modeling process, Gradient based Boosting Machines, GBM","lastPublishedDoi":"10.21203/rs.3.rs-8923546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8923546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the realm of semiconductor-based device compacts modeling, machine Learning-Driven (ML) standing as a pivotal force, promising a significant strides in predictive analysis capabilities. ML-based compact modeling offered a distinctive advantage in capturing of intricate relationships and patterns within extensively datasets. This paper introduced a pioneering designed approach centered on Gradient based Boosting Machines (GBM) to formulated a rapid and precisely compact model (CM). This framework presents a combustive yet computationally efficient methodology, showcases an \r\nemergent dynamically varying behavioral activity. The study underscored the viability of ML-driven compact models in emulating to the performance of conventional models for nano sized devices, particularly gate-all-around (GAA) nano sized sheet (NS) devices. The research extensively investigated the device characteristics of GAA NS devices, focused on various sources of process variability. The Key geometrical parameters like channel length, nano sized sheet width, and \r\nnano sized sheet thickness also served as input features for the GBM model. 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