Archivio Istituzionale della RicercaWe propose a machine learning-based framework to extract circuit-level VCSEL model parameters. The proposed approach predicts the parameters exploiting the light-current curve and small-signal modulation responses with two steps at constant and variable temperature, respectively. Promising results are achieved in terms of relative prediction error.
Machine Learning Assisted Extraction of Vertical Cavity Surface Emitting Lasers Parameters / Khan, Ihtesham; Tunesi, Lorenzo; Masood, Muhammad Umar; Ghillino, Enrico; Carena, Andrea; Curri, Vittorio; Bardella, Paolo. - ELETTRONICO. - (2022), pp. 1-2. (Intervento presentato al convegno 2022 IEEE Photonics Conference (IPC) tenutosi a Vancouver, BC, Canada nel 13-17 November 2022) [10.1109/IPC53466.2022.9975585].
Machine Learning Assisted Extraction of Vertical Cavity Surface Emitting Lasers Parameters
Khan, Ihtesham;Tunesi, Lorenzo;Masood, Muhammad Umar;Carena, Andrea;Curri, Vittorio;Bardella, Paolo
2022
Abstract
We propose a machine learning-based framework to extract circuit-level VCSEL model parameters. The proposed approach predicts the parameters exploiting the light-current curve and small-signal modulation responses with two steps at constant and variable temperature, respectively. Promising results are achieved in terms of relative prediction error.
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https://hdl.handle.net/11583/2976007