We propose machine learning technique for assessment of QoT impairments of integrated circuits. We consider margin reduction problem applied to a switching component. Overall results and data sets for machine-learning training are obtained by leveraging the integrated software environment of the Synopsys Photonic Design Suite.

Effectiveness of Machine Learning in Assessing QoT Impairments of Photonics Integrated Circuits to Reduce System Margin / Khan, Ihtesham; Chalony, Maryvonne; Ghillino, Enrico; Masood, Muhammad Umar; Patel, Jigesh; Richards, Dwight; Mena, Pablo; Bardella, Paolo; Carena, Andrea; Curri, Vittorio. - ELETTRONICO. - (2020), pp. 1-2. ((Intervento presentato al convegno IEEE Photonics Conference (IPC) tenutosi a CANADA [10.1109/IPC47351.2020.9252247].

Effectiveness of Machine Learning in Assessing QoT Impairments of Photonics Integrated Circuits to Reduce System Margin

Khan, Ihtesham;Masood, Muhammad Umar;Bardella, Paolo;Carena, Andrea;Curri, Vittorio
2020

Abstract

We propose machine learning technique for assessment of QoT impairments of integrated circuits. We consider margin reduction problem applied to a switching component. Overall results and data sets for machine-learning training are obtained by leveraging the integrated software environment of the Synopsys Photonic Design Suite.
978-1-7281-5891-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2853144