{
  "abstract": "We present a multiobjective, physics-informed optimization framework for oxide-based memristive devices that jointly minimizes forming voltage (Vf) and maximizes memory contrast (HRS/LRS). The proposed approach integrates COMSOL Multiphysics with MATLAB through a genetic algorithm (GA), enabling self-consistent electrothermal simulations and systematic exploration of a high-dimensional design space encompassing geometrical, electrical, thermal, and operational parameters. By optimizing 13 key variables, the GA efficiently identifies optimal trade-offs between switching efficiency and resistance ratio. The optimization results demonstrate a substantial enhancement in device performance, with the high-resistance-state to low-resistance-state ratio (HRS/LRS) improved by up to 21× relative to the reference device, while alternative optimized configurations achieve a reduction in Vf down to 0.7× of the reference value. A parameter-importance analysis based on a random forest model highlights oxide thickness and effective electrical and thermal conductivities as influential parameters. Importantly, the optimization further reveals nontrivial multiparameter trade-offs, demonstrating that reductions in Vf and enhancements in HRS/LRS can be simultaneously achieved through coordinated tuning of geometrical, material, and pulse-related parameters beyond simple intuitive design rules. Overall, the MATLAB–COMSOL co-optimization strategy provides a scalable and generalizable computational design methodology for next-generation low-power, high-contrast RRAM devices, and offers quantitative insights into the coupled physical mechanisms underlying filament formation and rupture.",
  "abstract_shown": true,
  "affiliations": [
    "University of Oklahoma , , , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Armin Gooran-Shoorakchaly"
    },
    {
      "affs": [
        0
      ],
      "name": "Sarah Sharif"
    },
    {
      "affs": [
        0
      ],
      "name": "Yaser Banad",
      "orcid": "0000-0001-7339-810X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aeybjg56gjyeb.bib",
  "cited_by": 0,
  "doi": "10.1021/acsomega.6c01327",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13717800/?tool=EBI",
  "id": "aeybjg56gjyeb",
  "issns": [
    "2470-1343"
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  "landing_url": "https://pubs.acs.org/acsodf/article/11/42/64416/5449402/Physics-Informed-Multi-Parameter-Optimization-of",
  "language": "eng",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "heading",
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      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
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  "openalex": "W7221533935",
  "pdf_url": "https://pubs.acs.org/doi/pdf/10.1021/acsomega.6c01327",
  "pmcid": "PMC13717800",
  "pmid": "42959657",
  "published": "2026-10-12",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/aeybjg56gjyeb.ris",
  "sources": [
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      "first_seen": "2026-10-30T12:18:39Z",
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      "source": "crossref",
      "url": "https://pubs.acs.org/acsodf/article/11/42/64416/5449402/Physics-Informed-Multi-Parameter-Optimization-of"
    },
    {
      "first_seen": "2026-10-31T00:20:37Z",
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      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13717800"
    },
    {
      "first_seen": "2026-11-01T00:20:52Z",
      "native_id": "MED:42959657",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42959657"
    },
    {
      "first_seen": "2026-11-02T12:22:03Z",
      "native_id": "PMC13717800",
      "source": "pmc",
      "url": "https://doi.org/10.1021/acsomega.6c01327"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2470-1343",
      "subfield": "general"
    }
  ],
  "title": "Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms",
  "title_text": "Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:22:03Z",
  "url": "https://research.draconitic.com/paper/aeybjg56gjyeb",
  "venue": "ACS Omega",
  "versions": []
}