{"paper_id":"0da69eec-d91f-466d-8ca1-f80e8079429c","body_text":"Sequence Detection in Bilayer 1T1R RRAM Device with Integrated State Machine | 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 Sequence Detection in Bilayer 1T1R RRAM Device with Integrated State Machine Simranjeet Singh, Godwin Paul, Ankit Bende, Tim Kempen, Felix Cüppers, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4194643/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Recent advancements in machine learning algorithms have driven a search for novel computation hardware and techniques to alleviate data bottlenecks. In terms of hardware, there’s a noticeable trend towards in-memory computing (IMC). Resistive random-access memory (RRAM) has surfaced as a compelling option for IMC applications owing to its non-volatile nature, reduced power requirements, and more compute density in contrast to conventional CMOS-based computing approaches. Simultaneously, there has been a notable rise in the advancement of novel algorithms alternatives to neuron-based implementations based on learning automata (e.g., propositional logic-based Tselin machine), presenting resource efficiency, particularly within the domain of the state-machine paradigm. An important observation is the multi-level behavior exhibited by RRAM cells, offering an avenue to mimic state machine functionalities. However, integrating state machines onto RRAM devices poses challenges despite these advancements. Through experimental explorations, this paper investigates the practical utilization of RRAM’s multi-state properties for finite-state machine (FSM) applications. This study demonstrates the multi-state behavior through gradual reset methodologies by fabricating RRAM cells alongside CMOS transistors to create 1T1R cells. Moreover, a sequence detector is devised to identify specific patterns within strings using a single 1T1R cell, effectively encapsulating the FSM onto a single device. This approach promises improvements in data density and offers enhanced energy efficiency. Rigorous experimental validation underscores the accuracy and reliability of the proposed methodology, laying a robust groundwork for efficient multi-state applications within advanced computing paradigms. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 08 Apr, 2024 Editor assigned by journal 08 Apr, 2024 Editor invited by journal 08 Apr, 2024 Submission checks completed at journal 08 Apr, 2024 First submitted to journal 31 Mar, 2024 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-4194643\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":289751759,\"identity\":\"c7d1893f-2b21-400a-af89-c877f13ee329\",\"order_by\":0,\"name\":\"Simranjeet 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