Robust calibration of multiparameter sensors via machine learning at the single-photon level

TL;DR

This paper demonstrates a neural network approach for calibrating complex multiparameter photonic sensors at the single-photon level, simplifying the calibration process without detailed device modeling.

Contribution

It introduces a neural network-based calibration method for integrated photonic sensors with multiple parameters, showing its effectiveness and reliability.

Findings

Reliable calibration achieved with proper training strategies

Neural network effectively maps device response to parameters

Applicable to sensors with complex transduction functions

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.