Archivio Istituzionale della RicercaWe propose a machine learning-based framework to acquire parameters that define stationary-and-dynamic behavior of VCSEL. Circuit-level simulations of light-current and S21 are used to train the model. In terms of relative-prediction-error promising results are achieved.
A Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL / Khan, Ihtesham; Marchisio, Andrea; Tunesi, Lorenzo; Masood, MUHAMMAD UMAR; 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_AT.2023.JW2A.141].
A Machine Learning-Based Model for Characterizing Stationary-and-Dynamic Behavior of VCSEL
Ihtesham Khan;Andrea Marchisio;Lorenzo Tunesi;Muhammad Umar Masood;Vittorio Curri;Andrea Carena;Paolo Bardella
2023
Abstract
We propose a machine learning-based framework to acquire parameters that define stationary-and-dynamic behavior of VCSEL. Circuit-level simulations of light-current and S21 are used to train the model. In terms of relative-prediction-error promising results are achieved.
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C_CLEO_A_Machine_Learning_Based_Model_for_Characterizing_Stationary_and_Dynamic_Behavior_of_VCSEL_02112022 (1).pdf
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https://hdl.handle.net/11583/2980624