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

Year: 2021

Authors: Cimini V., Polino E., Valeri M., Gianani I., Spagnolo N., Corrielli G., Crespi A., Osellame R., Barbieri M., Sciarrino F.

Autors Affiliation: Univ Roma Tre, Dipartimento Sci, Via Vasca Navale 84, I-00146 Rome, Italy; Sapienza Univ Roma, Dipartimento Fis, Piazzale Aldo Moro 5, I-00185 Rome, Italy; CNR, Ist Foton & Nanotecnol, IFN CNR, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy; Politecn Milan, Dipartimento Fis, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy; CNR, Ist Nazl Ottica, Largo Enrico Fermi 6, I-50125 Florence, Italy.

Abstract: 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

Journal/Review: PHYSICAL REVIEW APPLIED

Volume: 15 (4)      Pages from: 44003-1  to: 44003-10

More Information: This work is supported by the European Research Council (ERC) Advanced Grant CAPABLE (Composite integrated photonic platform by femtosecond laser micro-machining, Grant Agreement No. 742745), by the Amaldi Research Center, funded by the Ministero dell’Istruzione dell’Universita e della Ricerca (Ministry of Education, University and Research) program Dipartimento di Eccellenza (CUP:B81I18001170001), and by the European Union’s Horizon 2020 research and innovation program under the PHOQUSING (PHOtonic Quantum SamplING machine) project Grant Agreement No. 899544 and the STORMYTUNE (Spectral-Temporal Metrology with Tailored Quantum Measurements) project Grant Agreement No. 899587. I.G. is supported by the Ministero dell’Istruzione, dell’Universita e della Ricerca Grant of Excellence Departments (ARTICOLO 1, COMMI 314337 LEGGE 232/2016). N.S. acknowledges funding from Sapienza Universita via Bando Ricerca 2018: Progetti di Ricerca Piccoli, project Multiphase estimation in multiarm interferometers.
KeyWords: Neural-network; Quantum
DOI: 10.1103/PhysRevApplied.15.044003

ImpactFactor: 4.931
Citations: 30
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-03
References taken from IsiWeb of Knowledge: (subscribers only)

Connecting to view paper tab on IsiWeb: Click here
Connecting to view citations from IsiWeb: Click here