{
  "abstract": "CdSnP2-based integrated solar cell-photodetector (SC-PD) devices employing CdS and CuGaSe2 (CGS) as the window and back surface field (BSF) layers, respectively are investigated using a hybrid machine learning (ML)-assisted SCAPS-1D framework. Device optimization is performed by varying the thickness, doping concentration, and defect density of individual layers. SCAPS-generated data are used to train six ML models and one deep learning model, with ensemble-based algorithms exhibiting the highest predictive accuracy. The ML-guided optimization identifies the n-CdS/p-CdSnP2 (CTP)/p+-CGS architecture as the optimum configuration among thirteen candidate structures. Incorporation of a 200 nm CGS BSF layer significantly enhances both photovoltaic and photodetection performance, increasing the efficiency from 20.67% to 32.69%, responsivity from 0.53 AW-1 to 0.72 AW-1, and detectivity from 2.51x1014 Jones to 1.78x1016 Jones. SHapley Additive exPlanations (SHAP) analysis reveales that band-offset engineering, particularly at the window/absorber and absorber/BSF interfaces, together with absorber properties, governs device performance. These findings demonstrate the potential of CdSnP2 and the proposed data-driven SCAPS-ML framework for the accelerated design of high-efficiency multifunctional optoelectronic devices.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03642",
  "author_total": 4,
  "authors": [
    {
      "name": "Md. Alamin Hossain Pappu"
    },
    {
      "name": "Kazi Abrar Shafin"
    },
    {
      "name": "Mainul Hossain"
    },
    {
      "name": "Jaker Hossain"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bs2j2tt5hsp3e.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03642",
  "fulltext_url": null,
  "id": "bs2j2tt5hsp3e",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03642",
  "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",
      "2208|Chalcogenide Semiconductor Thin Films"
    ]
  ],
  "openalex": "W7208793866",
  "pdf_url": "https://arxiv.org/pdf/2609.03642",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bs2j2tt5hsp3e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.03642",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03642"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Machine learning-assisted design and explainable optimization of CdSnP2-based integrated solar-photodetector devices",
  "title_text": "Machine learning-assisted design and explainable optimization of CdSnP2-based integrated solar-photodetector devices",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/bs2j2tt5hsp3e",
  "venue": "arXiv",
  "versions": []
}