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 d...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-01, Vol.23 (3), p.1119
Main Authors: Homrighausen, Jonas, Horsthemke, Ludwig, Pogorzelski, Jens, Trinschek, Sarah, Glösekötter, Peter, Gregor, Markus
Format: Article
Language:English
Subjects:
Continuous radiation
Control data (computers)
Data acquisition
Data recording
Diamond machining
Diamonds
edge machine learning
Lasers
Low cost
Machine learning
Magnetic fields
Magnetic measurement
magnetometry
microcontroller
Microdiamonds
Neural networks
NV center in diamond
optically detected magnetic resonance
Point defects
quantum sensing
Radiation
Sensors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23031119