Photodetector Performance Prediction with Machine Learning

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dc.contributor.authorSimsek, Ergun
dc.contributor.authorMahabadi, Seyed Ehsan Jamali
dc.contributor.authorCarruthers, Thomas F.
dc.contributor.authorMenyuk, Curtis
dc.date.accessioned2025-06-05T14:02:49Z
dc.date.available2025-06-05T14:02:49Z
dc.date.issued2021-11-01
dc.descriptionFrontiers in Optics 2021 Washington, DC United States
dc.description.abstractFour machine learning algorithms are tested to predict the performance metrics of modified uni-traveling carrier photodetectors from their design parameters. The highest accuracy (>94%) is achieved with artificial neural networks.
dc.description.urihttps://opg.optica.org/abstract.cfm?uri=FiO-2021-FTu6C.4
dc.format.extent2 pages
dc.genreconference papers and proceedings
dc.identifierdoi:10.13016/m211w2-bora
dc.identifier.citationSimsek, Ergun, Seyed Ehsan Jamali Mahabadi, Thomas F. Carruthers, and Curtis R. Menyuk. “Photodetector Performance Prediction with Machine Learning.” Frontiers in Optics + Laser Science 2021 (2021), Paper FTu6C.4, November 1, 2021, FTu6C.4. https://doi.org/10.1364/FIO.2021.FTu6C.4.
dc.identifier.urihttps://doi.org/10.1364/FIO.2021.FTu6C.4
dc.identifier.urihttp://hdl.handle.net/11603/38602
dc.language.isoen_US
dc.publisherOptica
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectPhase noise
dc.subjectUMBC Optical Fiber Communications Laboratory
dc.subjectUMBC Computational Photonics Laboratory
dc.subjectNeural networks
dc.subjectUltrashort pulses
dc.subjectMachine learning
dc.subjectDistortion
dc.subjectPhotodetectors
dc.titlePhotodetector Performance Prediction with Machine Learning
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-9075-7071
dcterms.creatorhttps://orcid.org/0000-0003-4718-6976
dcterms.creatorhttps://orcid.org/0000-0002-5002-1657
dcterms.creatorhttps://orcid.org/0000-0003-0269-8433

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