Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond
- Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
Author: | Jonas Homrighausen, Ludwig Horsthemke, Jens Pogorzelski, Sarah Trinschek, Peter Glösekötter, Markus Gregor |
---|---|
URL: | https://www.mdpi.com/1424-8220/23/3/1119 |
DOI: | https://doi.org/10.3390/s23031119 |
Parent Title (English): | Sensors |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2023/01/19 |
Year of first Publication: | 2023 |
Provider of the Publication Server: | FH Münster - University of Applied Sciences |
Release Date: | 2023/01/19 |
Volume: | 23 |
Issue: | 3 |
Faculties: | Elektrotechnik und Informatik (ETI) |
Physikingenieurwesen (PHY) | |
Publication list: | Glösekötter, Peter |
Gregor, Markus | |
Trinschek, Sarah |