Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32$\mathrm{nT}/\sqrt{\mathrm{Hz}}$ and a theoretical shot noise limited sensitivity of 2.87 $\mathrm{nT}/\sqrt{\mathrm{Hz}}$ is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) ${\mathrm{mm}}^3$ combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around $0.1\mathrm{W}$, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.

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.

In quantum sensing of magnetic fields, ensembles of nitrogen-vacancy centers in diamond offer high sensitivity, high bandwidth and outstanding spatial resolution while operating in harsh environments. Moreover, the orientation of defect centers along four crystal axes forms an intrinsic coordinate system, enabling vector magnetometry within a single diamond crystal. While most vector magnetometers rely on a known bias magnetic field for full recovery of three-dimensional (3D) field information, employing external 3D Helmholtz coils or permanent magnets results in bulky, laboratory-bound setups, impeding miniaturization of the device. Here, a novel approach is presented that utilizes a fiber-integrated microscale coil at the fiber tip to generate a localized uniaxial magnetic field. The same fiber-tip coil is used in parallel for spin control by combining dc and microwave signals in a bias tee. To implement vector magnetometry using a uniaxial bias field, the orientation of the diamond crystal is preselected and then fully characterized by rotating a static magnetic field in three planes of rotation. The measurement of vector magnetic fields in the full solid angle is demonstrated with a shot-noise-limited sensitivity of $19.4{\text{nT/Hz}}^{1/2}$ and microscale spatial resolution while achieving a fiber sensor head cross section of less than $1{\text{mm}}^2$.

This paper presents the application of a compact and fully integrated LED quantum sensor based on the NV centers in diamond for current measurement in a busbar. The magnetic field measurements from the sensor are directly compared with measurements from a numerical simulation, eliminating the need for calibration. The sensor setup achieves an accuracy of 0.28% in the measurement range of 0–30 $\mathrm{A}$ DC. The integration of advanced quantum sensing technology with practical current measurement demonstrates the potential of this sensor for applications in electrical and distribution networks.

Nitrogen vacancy (NV) diamonds are promising room temperature quantum sensors. As the technology moves towards application, efficient use of energy and cost become critical for miniaturization. This work focuses on microwave-based spin control using the short-circuited end of a slot line, analyzed by finite element method (FEM) for magnetic field amplitude and uniformity. A microstrip-to-slot-line converter with a $-10$ dB bandwidth of $3.2$ GHz was implemented. Rabi oscillation measurements with an NV microdiamond on a glass fiber show uniform excitation over $1.5$ MHz across the slot, allowing spin manipulation within the coherence time of the NV center.

This work presents a conceptual design and preliminary analysis of a resource-efficient measurement device using nitrogen vacancy centers to measure magnetic fields. The electric current is derived by using the Biot–Savart law. Laboratory instruments have broad measurement capabilities, like frequency bandwidth and accuracy, but are often oversized and expensive. Field-programmable gate arrays with dedicated fast analog-to-digital and digital-to-analog converters have limitations in terms of the frequency bandwidth of generated signals and the digitisation of analog signals. We use a RedPitaya field-programmable gate array with an add-on board which includes an IQ Modulator. The high-frequency signals have a typical frequency around 2.87 GHz and a limited bandwidth of 100 MHz. Due to the limited bandwidth, it is not possible to detect the whole optically detected magnetic resonance spectrum of the nitrogen vacancy center with its eight resonances. In this work, we show that it is possible to derive the electric current and the temperature from only four resonances, instead of the full optically detected magnetic resonance spectrum. The proposed approach significantly reduces hardware complexity while maintaining a root mean square error of 3.80 A, obtained over a temperature range of −25 °C to 50 °C.