Robust calibration of multiparameter sensors via machine learning at the single-photon level
Abstract
Calibration of sensors is a fundamental step to validate their operation. This can be a demanding task, as it relies on acquiring a detailed modelling of the device, aggravated by its possible dependence upon multiple parameters. Machine learning provides a handy solution to this issue, operating a mapping between the parameters and the device response, without needing additional specific information on its functioning. Here we demonstrate the application of a Neural Network based algorithm for the calibration of integrated photonic devices depending on two parameters. We show that a reliable characterization is achievable by carefully selecting an appropriate network training strategy. These results show the viability of this approach as an effective tool for the multiparameter calibration of sensors characterized by complex transduction functions.
Keywords
Cite
@article{arxiv.2009.07122,
title = {Robust calibration of multiparameter sensors via machine learning at the single-photon level},
author = {Valeria Cimini and Emanuele Polino and Mauro Valeri and Ilaria Gianani and Nicolò Spagnolo and Giacomo Corrielli and Andrea Crespi and Roberto Osellame and Marco Barbieri and Fabio Sciarrino},
journal= {arXiv preprint arXiv:2009.07122},
year = {2021}
}