{
  "abstract": "Compact delay elements remain a central challenge in photonic neuromorphic processors. Here, we demonstrate an optoelectronic delayed-feedback reservoir computer that incorporates a foundry-fabricated silicon nitride (Si$_3$N$_4$) true-time-delay circuit within its feedback loop. Eight cascaded Archimedean spirals provide a \\SI{1.76}{\\metre} on-chip optical path and a calculated passive group delay of \\SI{11.63}{\\nano\\second}. At a feedback gain of $G=0.0223$, the system classifies sinusoidal and square waveforms without error (word error rate, WER\\,$=$\\,0 across all cross-validation folds), predicts the Mackey--Glass chaotic series with a best-fold normalized mean-square error (NMSE) of $0.0082$, and performs nine-class Japanese Vowels speaker classification with WER\\,$=$\\,0.0898. We also introduce a subcarrier phase-encoding method that maps the calculated $0.232π$ spiral-to-reference phase contrast onto the measured reservoir states through deliberate aliasing, achieving NMSE\\,$=$\\,0.0767 and WER\\,$=$\\,0 without increasing the insertion-loss-limited feedback gain. These results show that on-chip propagation delay in Si$_3$N$_4$ can operate as a functional component of an optoelectronic reservoir computer and motivate lower-loss, more-integrated implementations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.05907",
  "author_total": 3,
  "authors": [
    {
      "name": "Ahmad Murad"
    },
    {
      "name": "Shadad Watad"
    },
    {
      "name": "Alina Karabchevsky"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2vdfnha9jm7z4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05907",
  "fulltext_url": null,
  "id": "2vdfnha9jm7z4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.05907",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7212071530",
  "pdf_url": "https://arxiv.org/pdf/2609.05907",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2vdfnha9jm7z4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.05907",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05907"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Optoelectronic Reservoir Computing with an On-Chip True-Time-Delay Element",
  "title_text": "Optoelectronic Reservoir Computing with an On-Chip True-Time-Delay Element",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/2vdfnha9jm7z4",
  "venue": "arXiv",
  "versions": []
}