Archivio Istituzionale della RicercaModern optical transmission standards require steep band-pass filters enabling spectrally efficient channels spacing. For this aim, we propose a machine-learning agent to assist in the characterization of complex ring resonator filters to fulfill the transmission requirements.
Machine Learning aided characterization of multi-stage integrated ring resonator filters / Khan, Ihtesham; Tunesi, Lorenzo; Masood, MUHAMMAD UMAR; Ghillino, Enrico; Bardella, Paolo; Carena, Andrea; Curri, Vittorio. - ELETTRONICO. - (2022). (Intervento presentato al convegno Optica Advanced Photonics Congress 2022 tenutosi a Maastricht,Limburg Netherlands nel 24–28 July 2022).
Machine Learning aided characterization of multi-stage integrated ring resonator filters
Ihtesham Khan;Lorenzo Tunesi;Muhammad Umar Masood;Paolo Bardella;Andrea Carena;Vittorio Curri
2022
Abstract
Modern optical transmission standards require steep band-pass filters enabling spectrally efficient channels spacing. For this aim, we propose a machine-learning agent to assist in the characterization of complex ring resonator filters to fulfill the transmission requirements.
| File | Dimensione | Formato | |
|---|---|---|---|
|
IPRSN-2022-IM3B.4.pdf
accesso riservato
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
235.09 kB
Formato
Adobe PDF
|
235.09 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
C_CLEO_2022_Machine_Learning_Assisted_Design___V1_29112021_2.pdf
Open Access dal 29/07/2023
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
227.22 kB
Formato
Adobe PDF
|
227.22 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/11583/2972638