Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

Calibration of sensors is a fundamental step in validating their operation. This can be a demanding task, as it relies on acquiring detailed modeling of the device, which can be 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. Furthermore, the approach is proven to be versatile and promising for mass production, as the same neural network is able to calibrate different devices that have the same

Automated summary

Calibrating sensors is a crucial step in validating their functionality, and machine learning offers a convenient solution by mapping parameters to device responses. This study demonstrates the use of a neural network algorithm for calibrating integrated photonic devices depending on two parameters, showing that reliable characterization is achievable with careful network training. The approach proves to be versatile and promising for mass production, as the same neural network can calibrate different devices with similar attributes.