Department of Engineering Physics, Münster University of Applied Sciences, Stegerwaldstraße 39, 48565 Steinfurt, Germany.
Department of Electrical Engineering and Computer Science, Münster University of Applied Sciences, Stegerwaldstraße 39, 48565 Steinfurt, Germany.
Sensors (Basel). 2023 Jan 18;23(3):1119. doi: 10.3390/s23031119.
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.
基于氮空位中心在纳米或微金刚石中的光探测磁共振（ODMR）的量子磁强计是一种用于精确磁场传感器的有前途的技术。在这里，我们提出了一种新的、低成本的独立传感器设置，该设置在嵌入式设备上使用机器学习，即边缘机器学习。我们使用从连续波 ODMR 设置中获取的数据训练人工神经网络，然后在传感器设备上使用这个预先训练的网络来从记录的 ODMR 光谱中推断磁场的大小。在我们提出的传感器设置中，使用低成本、低功耗的 ESP32 微控制器开发板来控制数据记录并执行网络的推断。在概念验证研究中，我们表明该设置能够高精度地测量磁场，并且有可能实现具有广泛测量范围的稳健且易于使用的传感器应用。
