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10 March 2023 Two-step machine learning assisted extraction of VCSEL parameters
Ihtesham Khan, Muhammad Umar Masood, Lorenzo Tunesi, Enrico Ghillino, Andrea Carena, Vittorio Curri, Paolo Bardella
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Proceedings Volume 12415, Physics and Simulation of Optoelectronic Devices XXXI; 124150P (2023) https://doi.org/10.1117/12.2650220
Event: SPIE OPTO, 2023, San Francisco, California, United States
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Abstract
We propose a Machine Learning (ML) assisted procedure to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step algorithm to ensure high accuracy of the prediction. In the first step, temperature effects are not included and a Deep Neural Network (DNN) is trained on a dataset of 10000 mean-field VCSEL simulations, obtained changing nine temperature-independent parameters. The agent is used to retrieve those parameters from experimental results at a fixed temperature. Secondly, additional nine temperature-dependent parameters are analyzed while keeping as constant the extracted ones and changing the operation temperature. In this way a second dataset of 10000 simulations is created and a new agent in trained to extract those parameters from temperature-dependent L-I and S21 curves.
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Ihtesham Khan, Muhammad Umar Masood, Lorenzo Tunesi, Enrico Ghillino, Andrea Carena, Vittorio Curri, and Paolo Bardella "Two-step machine learning assisted extraction of VCSEL parameters", Proc. SPIE 12415, Physics and Simulation of Optoelectronic Devices XXXI, 124150P (10 March 2023); https://doi.org/10.1117/12.2650220
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KEYWORDS
Vertical cavity surface emitting lasers
Machine learning
Simulations
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