High Storage and Energy Efficient Memory for Cryogenic Computing

High Storage and Energy Efficient Memory for Cryogenic Computing | 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 Physical Sciences - Article High Storage and Energy Efficient Memory for Cryogenic Computing Qing-Tai Zhao, Yi Han, Jingxuan Sun, Benjamin Richstein, Jin Bae, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3300928/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Efficient computing in cryogenic environments, encompassing classical von Neumann architectures, advanced quantum and neuromorphic systems, holds the potential to revolutionize big data processing. As the demand for high storage density and energy-efficient memories grows, the absence of a clear solution for cryogenic memory remains a challenge. Here, we present a cryogenic capacitorless Random Access Memory (C 2 RAM) utilizing advanced Si technology. This innovation is positioned to reshape cryogenic computing, with its high scalability and the capacity to be written and erased across multiple-states, significantly boosting the storage density. Notably, the C 2 RAM requires only ultra-low write energies, measuring just a few zeptojoules and provides exceptionally long retention times preserving data for over a decade. This positions C 2 RAM as a prime contender for nonvolatile memory for cryogenic von Neumann architectures and quantum technologies. In addition, the memory unit emulates biological synapses, including potentiation and depression, enabling a seamless integration into crossbar arrays, requiring no additional selectors for neuromorphic computing. The fusion of logic and analog capabilities unlocks substantial potential for high-density cryogenic memory applications. This innovative breakthrough enables the convergence of classical von Neumann, quantum, and neuromorphic computing, harnessing the significant performance advantages anticipated at cryogenic temperatures. Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Physical sciences/Engineering/Electrical and electronic engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationfinal.docx High Storage and Energy Efficient Memory for Cryogenic Computing Cite Share Download PDF Status: Under Review 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. 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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-3300928","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":240141366,"identity":"1ba297b5-7e6b-4c58-93c4-795e44716892","order_by":0,"name":"Qing-Tai 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