TY  - JOUR
A1  - Homrighausen, Jonas
A1  - Horsthemke, Ludwig
A1  - Pogorzelski, Jens
A1  - Trinschek, Sarah
A1  - Glösekötter, Peter
A1  - Gregor, Markus
T1  - Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond
JF  - Sensors
N2  - 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.
Y1  - 2023
UR  - https://www.mdpi.com/1424-8220/23/3/1119
U6  - http://dx.doi.org/10.3390/s23031119
VL  - 23
IS  - 3
ER  -