We propose a machine learning-based framework to predict the fabrication uncertainty and evaluate the effective-index shift in multi-ring integrated filtering elements. Excellent results are achieved in predicting each ring’s effective-index shift.
Machine Learning Aided Prediction of Fabrication Uncertainties in Integrated Multi-Ring Filters / Tunesi, Lorenzo; Khan, Ihtesham; Masood, MUHAMMAD UMAR; Marchisio, Andrea; Ghillino, Enrico; Curri, Vittorio; Carena, Andrea; Bardella, Paolo. - ELETTRONICO. - (2023), pp. 1-2. (Intervento presentato al convegno CLEO: Science and Innovations tenutosi a San Jose, CA, United States nel 7-12 May 2023) [10.1364/CLEO_SI.2023.STh4H.2].
Machine Learning Aided Prediction of Fabrication Uncertainties in Integrated Multi-Ring Filters
Lorenzo Tunesi;Ihtesham Khan;Muhammad Umar Masood;Andrea Marchisio;Vittorio Curri;Andrea Carena;Paolo Bardella
2023
Abstract
We propose a machine learning-based framework to predict the fabrication uncertainty and evaluate the effective-index shift in multi-ring integrated filtering elements. Excellent results are achieved in predicting each ring’s effective-index shift.File | Dimensione | Formato | |
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C_CLEO_Machine_Learning_Approach_to_Predict_Fabrication_Uncertainties_in_Multi_Ring_Integrated_Filtering_Elements_19112022.pdf
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https://hdl.handle.net/11583/2980626