Archivio Istituzionale della RicercaWe 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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
IPCIEEE.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
547.66 kB
Formato
Adobe PDF
|
547.66 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
IPCV2.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
578.82 kB
Formato
Adobe PDF
|
578.82 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2853144