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

doi:10.1109/IPC47351.2020.9252247

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].

Titolo:	Effectiveness of Machine Learning in Assessing QoT Impairments of Photonics Integrated Circuits to Reduce System Margin
Autori:	KHAN, IHTESHAM Chalony, Maryvonne Ghillino, Enrico MASOOD, MUHAMMAD UMAR Patel, Jigesh Richards, Dwight Mena, Pablo BARDELLA, PAOLO CARENA, ANDREA CURRI, VITTORIO mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2020
Abstract:	We propose machine learning technique for assessment of QoT impairments of integrated circuits. W...e 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.
ISBN:	978-1-7281-5891-4
Appare nelle tipologie:	4.1 Contributo in Atti di convegno

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