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We train, evaluate and demonstrate the ML models on thirty-six private datasets from three previously published devices, which denote current responses under illumination and ambient temperature of graphene oxide (GO) doped p-Si Schottky barrier diodes (SBDs). The GO doping levels are 0%, 1%, 3%, 5%, and 10%. The illumination ranged from dark (0 mW\/cm<jats:sup>2<\/jats:sup>) to 30 mW\/cm<jats:sup>2<\/jats:sup>. The predictions are then made completely at the intensity of 60 mW\/cm<jats:sup>2<\/jats:sup>. For each diode, some values of the barrier height (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\phi $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>\u03d5<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>), ideality factor (<jats:italic>n<\/jats:italic>), and series resistance (<jats:inline-formula><jats:alternatives><jats:tex-math>$$R_s$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mi>s<\/mml:mi>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) independently calculated using the Cheung\u2013Cheung method were included in the training dataset. The predictions are done at unspecified intensities on the model development data at 80 and 100 mW\/cm<jats:sup>2<\/jats:sup>, and on external data at 5% and 20% GO doping which were not part of the development dataset. The ANN achieved a mean square error and mean absolute error score below 0.003 across all datasets. This demonstrates the effective learning capabilities of the ANN models in accurately capturing the photo responses of the photodiodes and accurately predicting the internal parameters of the Schottky Barrier Diodes (SBDs), all without relying on an inherent understanding of the thermionic emission (TE) equation for SBDs. The ANN models achieved high accuracy in this process. The proposed ML models can significantly reduce analysis time in device development cycles and can be applied to other datasets in various fields.<\/jats:p>","DOI":"10.1038\/s41598-023-41111-7","type":"journal-article","created":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T12:01:51Z","timestamp":1693051311000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine learning models for efficient characterization of Schottky barrier photodiode internal parameters"],"prefix":"10.1038","volume":"13","author":[{"given":"Richard O.","family":"Ocaya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andronicus A.","family":"Akinyelu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah G.","family":"Al-Sehemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ay\u015fegul","family":"Dere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A.","family":"Al-Ghamdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahrettin","family":"Yakuphano\u011flu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,26]]},"reference":[{"key":"41111_CR1","doi-asserted-by":"publisher","unstructured":"Ping, H., Stoyanovich, J. & Howe, B. 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