Archivio Istituzionale della RicercaA digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder modulator is obtained through machine learning techniques. The model is used to test optimization algorithms that automatically set the modulator control voltages under different operative conditions finding the optimum bias point.
Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators / D'Ingillo, Rocco; D'Amico, Andrea; Usmani, Fehmida; Borraccini, Giacomo; Straullu, Stefano; Siano, Rocco; Belmonte, Michele; Curri, Vittorio. - ELETTRONICO. - (2023), pp. 1-3. (Intervento presentato al convegno 2023 International Conference on Photonics in Switching and Computing (PSC) tenutosi a Mantova, Italy nel 26-29 September 2023) [10.1109/PSC57974.2023.10297214].
Machine Learning Aided Control of Ultra-Wideband Indium Phosphide IQ Mach-Zehnder Modulators
D'Ingillo, Rocco;D'Amico, Andrea;Usmani, Fehmida;Borraccini, Giacomo;Curri, Vittorio
2023
Abstract
A digital model of a dual-polarization IQ ultra-wideband indium phosphide Mach-Zehnder modulator is obtained through machine learning techniques. The model is used to test optimization algorithms that automatically set the modulator control voltages under different operative conditions finding the optimum bias point.
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Machine_Learning_Aided_Control_of_Ultra-Wideband_Indium_Phosphide_IQ_Mach-Zehnder_Modulators.pdf
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PSC2023_ML_Modulator-1.pdf
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https://hdl.handle.net/11583/2983886