The complexity scaling of silicon photonics circuits is raising novel needs related to control. Reconfigurable architectures need fast, accurate and robust procedures for the tuning and stabilization of their working point, counteracting temperature drifts originated by environmental fluctuations and mutual thermal crosstalk from surrounding integrated devices. In this contribution, we report on our recent achievements on the automated tuning, control and stabilization of silicon photonics architectures. The proposed control strategy exploits transparent integrated detectors to monitor non-invasively the light propagating in the silicon waveguides in key spots of the circuit. Local monitoring enables the partitioning of complex architectures in small photonic cells that can be easily tuned and controlled, with need for neither preliminary circuit calibration nor global optimization algorithms. The ability to monitor the Quality Of of Transmission (QoT) of the optical paths in Photonic Integrated Circuits (PICs) is also demonstrated with the use of channel labelling and non-invasive light monitoring. Several examples of applications are presented that include the automatic reconfiguration and feedback controlled stabilization of an 8×8 switch fabric based on Mach-Zehnder interferometers (MZIs) and the realization of a wavelength locking platform enabling feedback-control of silicon microring resonators (MRRs) for the realization of a 4×10 Gbit/s wavelength-division-multiplexing transmitter. The effectiveness and the robustness of the proposed approach for tuning and stabilization of the presented architectures is demonstrated by showing that no significant performance degradation is observed under uncooled operation for the silicon chip.

Automated tuning, control and stabilization of photonic integrated circuits

De Aguiar, Douglas O.;Annoni, Andrea;Peserico, Nicola;Guglielmi, Emanuele;Carminati, Marco;Ferrari, Giorgio;Morichetti, Francesco
2017-01-01

Abstract

The complexity scaling of silicon photonics circuits is raising novel needs related to control. Reconfigurable architectures need fast, accurate and robust procedures for the tuning and stabilization of their working point, counteracting temperature drifts originated by environmental fluctuations and mutual thermal crosstalk from surrounding integrated devices. In this contribution, we report on our recent achievements on the automated tuning, control and stabilization of silicon photonics architectures. The proposed control strategy exploits transparent integrated detectors to monitor non-invasively the light propagating in the silicon waveguides in key spots of the circuit. Local monitoring enables the partitioning of complex architectures in small photonic cells that can be easily tuned and controlled, with need for neither preliminary circuit calibration nor global optimization algorithms. The ability to monitor the Quality Of of Transmission (QoT) of the optical paths in Photonic Integrated Circuits (PICs) is also demonstrated with the use of channel labelling and non-invasive light monitoring. Several examples of applications are presented that include the automatic reconfiguration and feedback controlled stabilization of an 8×8 switch fabric based on Mach-Zehnder interferometers (MZIs) and the realization of a wavelength locking platform enabling feedback-control of silicon microring resonators (MRRs) for the realization of a 4×10 Gbit/s wavelength-division-multiplexing transmitter. The effectiveness and the robustness of the proposed approach for tuning and stabilization of the presented architectures is demonstrated by showing that no significant performance degradation is observed under uncooled operation for the silicon chip.
2017
Proceedings of SPIE - The International Society for Optical Engineering
9781510609853
Feedback control; microring resonators; optical performance monitoring; photonic integrated circuits; photonic switching systems; silicon photonics; wavelength locking; Electronic, Optical and Magnetic Materials; Condensed Matter Physics; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1037099
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