{
  "abstract": "Resistive random access memory (RRAM) devices, also known as memristors, are highly dependent on fabrication, location on the wafer, and measurement protocols. However, openly available large-scale memristor datasets remain scarce, particularly those that combine experimental metadata with electrical measurements and preserve explicit links across the experimental workflow. Here, we present a comprehensive relational database of automated electrical characterization data from oxide-based memristors. The database links 6,190 memristor devices across TiN/HfOx/TiN, Pt/TiOx/AlOy/Pt, and Pt/TiOx/Pt stacks to 161,006 validated experiments and over 169 million electrical point records. It integrates fabrication, wafer mapping, electrical characterization, and modeling to describe electroforming, current-voltage non-linearity, memory windows (R_off/R_on), switching dynamics, and short-term volatility. Released as a normalized and indexed SQLite database with schema documentation, graphical user interfaces, and examples of empirical modeling, this resource supports provenance-aware querying, statistical analysis, and data-driven applications for the development of future memristor technologies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01500",
  "author_total": 6,
  "authors": [
    {
      "name": "Lai Gan"
    },
    {
      "name": "Guoyang Huang"
    },
    {
      "name": "Deepika Yadav"
    },
    {
      "name": "Spyros Stathopoulos"
    },
    {
      "name": "Ben D. Rowlinson"
    },
    {
      "name": "Themis Prodromakis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bvhnyvt9b0xdb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01500",
  "fulltext_url": null,
  "id": "bvhnyvt9b0xdb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01500",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.mtrl-sci"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7206169375",
  "pdf_url": "https://arxiv.org/pdf/2609.01500",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bvhnyvt9b0xdb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "2609.01500",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01500"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.mtrl-sci",
      "subfield": "condensed-matter"
    }
  ],
  "title": "A relational fabrication-to-modeling database for memristor devices",
  "title_text": "A relational fabrication-to-modeling database for memristor devices",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/bvhnyvt9b0xdb",
  "venue": "arXiv",
  "versions": []
}