Simsek, ErgunMahabadi, Seyed Ehsan JamaliCarruthers, Thomas F.Menyuk, Curtis2025-06-172025-06-172021-11-01Simsek, Ergun, Seyed Ehsan Jamali Mahabadi, Thomas F. Carruthers, and Curtis R. Menyuk. "Photodetector Performance Prediction with Machine Learning" In Frontiers in Optics + Laser Science 2021 (2021), Paper FTu6C.4, FTu6C.4. Optica Publishing Group, 2021. https://doi.org/10.1364/FIO.2021.FTu6C.4.https://doi.org/10.1364/FIO.2021.FTu6C.4http://hdl.handle.net/11603/38991Frontiers in Optics 2021, Washington, DC United States, 1–4 November 2021Four 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.2 pagesen-USThis 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.Neural networksUMBC Optical Fiber Communications LaboratoryUMBC High Performance Computing Facility (HPCF)Phase noiseUMBC Computational Photonics LaboratoryUltrashort pulsesDistortionPhotodetectorsMachine learningText