  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.
