Controlling the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach

Controlling the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach | 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 Research Article Controlling the morphology transition on MOVPE-grown (100) β-Ga 2 O 3 film between step-flow growth and step-bunching: A machine learning-assisted approach Ta-Shun Chou, Saud Bin Anooz, Natasha Dropka, Han-Hsu Chen, Zbigniew Galazka, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5930250/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 The step-bunching instability in (100) β-Ga₂O₃ films grown via metalorganic vapor phase epitaxy (MOVPE) was investigated using a machine learning approach based on Random Forest (RF). The study reveals the interplay of Ga supersaturation (O 2 /Ga) and impurity effects as coexisting mechanisms driving the morphological transition (from step-flow growth to step-bunching). The developed machine-learning framework accurately classifies growth morphology and offers valuable insights by correlating theoretical principles with experimental parameters. Critical growth parameters influencing the film morphology were identified. The corresponding strategy, high Ga supersaturation, is proposed to mitigate the step-bunching formation and maintain the desired step-flow growth mode. Despite the challenges posed by small datasets, the RF model demonstrates robust classification performance, establishing machine learning as a powerful tool for experimental science. Electronic Materials and Devices Gallium Oxide MOVPE Random Forest Full Text Additional Declarations The authors declare no competing interests. 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. 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-5930250","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409112728,"identity":"f715e8d8-9aa0-47e1-ad8f-e14f09621ec1","order_by":0,"name":"Ta-Shun 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