29 citations · 47 across the 8 of their papers we have counts for
8 papers
Generation of electro-optic frequency combs with optimized flatness in a silicon ring resonator modulator
Erwan Weckenmann, Thyago Monteiro, Uiara Celine de Moura +9
The flatness of electro-optic frequency combs (EOFCs) generated in a single silicon ring resonator modulator (RRM) is optimized by employing harmonic superposition of the radio-fre…
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
Metodi Plamenov Yankov, Francesco Da Ros, Uiara Celine de Moura +2
The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows…
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
Ali Cem, Siqi Yan, Uiara Celine de Moura +3
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer mes…
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
Uiara Celine de Moura, Ann Margareth Rosa Brusin, Andrea Carena +2
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a singl…
Experimental characterization of Raman amplifier optimization through inverse system design
Uiara Celine de Moura, Francesco Da Ros, Ann Margareth Rosa Brusin +2
Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication…
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
Metodi P. Yankov, Uiara Celine de Moura, Francesco Da Ros
Cascades of a machine learning-based EDFA gain model trained on a single physical device and a fully differentiable stimulated Raman scattering fiber model are used to predict and…