Freiburg 2024 – scientific programme
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Q: Fachverband Quantenoptik und Photonik
Q 38: Poster IV
Q 38.17: Poster
Wednesday, March 13, 2024, 17:00–19:00, KG I Foyer
Edge Machine-Learning assisted Magnetometer Based on NV-Ensembles in Diamond — •Jonas Homrighausen1, Ludwig Horsthemke2, Jens Pogorzelski2, Sarah Trinschek1, Peter Glösekötter2, and Markus Gregor1 — 1Department of Engineering Physics, University of Applied Sciences, Münster — 2Department of Electrical Engineering and Computer Science
In the field of quantum sensing, particularly in magnetometry, the nitrogen-vacancy (NV) center in diamond stands out as a promising sensor material. It offers high sensitivity, exceptional spatial resolution, and wide bandwidth at room temperature, making it an ideal candidate for miniaturization and integration due to its solid-state host crystal. However, the real-time tracking of magnetic field strengths using optically detected magnetic resonance (ODMR) poses challenges, requiring sophisticated equipment such as multi-channel frequency modulated RF generators and lock-in techniques. Additionally, accurately calculating magnetic field magnitudes from transition frequencies requires various parameters like crystal orientation and internal strain parameters. To address these challenges, we propose a machine-learning assisted approach leveraging an ESP32 microcontroller as the central control and acquisition unit [1]. By performing inference on a pre-trained artificial neural network using data collected from a fiber-coupled NV ensemble, we obtain the local magnetic field magnitude at the fiber tip. By using off-the-shelf components, we present a low-cost, low-power standalone sensor device that can easily made portable.
[1] J. Homrighausen et al. (2023). Sensors 23(3), 1119.
Keywords: Quantum Sensing; NV Center; Artificial Neural Network; Fiber Sensor