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Corcione E, Jakob F, Wagner L, Joos R, Bisquerra A, Schmidt M, Wieck AD, Ludwig A, Jetter M, Portalupi SL, Michler P, Tarín C. Machine learning enhanced evaluation of semiconductor quantum dots. Sci Rep 2024; 14:4154. [PMID: 38378845 PMCID: PMC10879153 DOI: 10.1038/s41598-024-54615-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.
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Affiliation(s)
- Emilio Corcione
- Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany.
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany.
| | - Fabian Jakob
- Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Munich Institute of Robotics and System Intelligence, Technical University of Munich, Munich, Germany
| | - Lukas Wagner
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Raphael Joos
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Andre Bisquerra
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Marcel Schmidt
- Lehrstuhl für Angewandte Festkörperphysik, Ruhr Universität Bochum, Bochum, Germany
| | - Andreas D Wieck
- Lehrstuhl für Angewandte Festkörperphysik, Ruhr Universität Bochum, Bochum, Germany
| | - Arne Ludwig
- Lehrstuhl für Angewandte Festkörperphysik, Ruhr Universität Bochum, Bochum, Germany
| | - Michael Jetter
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Simone L Portalupi
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Peter Michler
- Institut für Halbleiteroptik und Funktionelle Grenzflächen, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
- Center for Integrated Quantum Science and Technology, University of Stuttgart, Stuttgart, Germany
| | - Cristina Tarín
- Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany
- Research Center SCoPE, University of Stuttgart, Stuttgart, Germany
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Corcione E, Pfezer D, Hentschel M, Giessen H, Tarín C. Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing. Sensors (Basel) 2021; 22:s22010007. [PMID: 35009555 PMCID: PMC8747440 DOI: 10.3390/s22010007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 05/11/2023]
Abstract
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.
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Affiliation(s)
- Emilio Corcione
- Research Center SCoPE, Institute for System Dynamics, University of Stuttgart, 70563 Stuttgart, Germany;
- Correspondence:
| | - Diana Pfezer
- Research Center SCoPE, 4th Physics Institute, University of Stuttgart, 70569 Stuttgart, Germany; (D.P.); (M.H.); (H.G.)
| | - Mario Hentschel
- Research Center SCoPE, 4th Physics Institute, University of Stuttgart, 70569 Stuttgart, Germany; (D.P.); (M.H.); (H.G.)
| | - Harald Giessen
- Research Center SCoPE, 4th Physics Institute, University of Stuttgart, 70569 Stuttgart, Germany; (D.P.); (M.H.); (H.G.)
| | - Cristina Tarín
- Research Center SCoPE, Institute for System Dynamics, University of Stuttgart, 70563 Stuttgart, Germany;
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