Ultrafast temperature field detection and identification is crucial for applications ranging from environmental sensing and biomedical monitoring to thermal management in advanced energy systems. Conventional temperature sensors-comprising discrete sensing arrays, data storage units, and external processors-suffer from high latency due to slow sensor response, repeated analog-to-digital conversions, and extensive data transmission inherent to von Neumann architectures. Here, we report a diamond array-based quantum sensor that integrates temperature sensing and real-time processing within a unified in-sensor computing (ISC) architecture. Exploiting the strong linear correlation between temperature and the zero-field splitting of nitrogen-vacancy (NV) color center centers in diamond, we realize a fixed-frequency temperature sensor with ultrafast response and tunable responsivity, enabled by multi-parameter microwave modulate. Matrix-vector multiplication of temperature intensity and responsivity, combined with Kirchhoff's current summation, enables direct execution of neural-network-style computations on sensed data. The proposed system achieves a single-shot detection and identification latency of just 196.8 μs, as experimentally validated. This work demonstrates a scalable ISC-enabled quantum sensing paradigm, offering a promising route toward high-speed, low-power intelligent temperature field detection.
Ultrafast temperature field detection and identification is crucial for applications ranging from environmental sensing and biomedical monitoring to thermal management in advanced energy systems. Conventional temperature sensors-comprising discrete sensing arrays, data storage units, and external processors-suffer from high latency due to slow sensor response, repeated analog-to-digital conversions, and extensive data transmission inherent to von Neumann architectures. Here, we report a diamond array-based quantum sensor that integrates temperature sensing and real-time processing within a unified in-sensor computing (ISC) architecture. Exploiting the strong linear correlation between temperature and the zero-field splitting of nitrogen-vacancy (NV) color center centers in diamond, we realize a fixed-frequency temperature sensor with ultrafast response and tunable responsivity, enabled by multi-parameter microwave modulate. Matrix-vector multiplication of temperature intensity and responsivity, combined with Kirchhoff's current summation, enables direct execution of neural-network-style computations on sensed data. The proposed system achieves a single-shot detection and identification latency of just 196.8 μs, as experimentally validated. This work demonstrates a scalable ISC-enabled quantum sensing paradigm, offering a promising route toward high-speed, low-power intelligent temperature field detection.
Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detected magnetic resonance (ODMR) spectra, where target quantities are encoded in resonance features. Conventional nonlinear fitting is often computationally expensive, sensitive to initialization, and prone to failure at low signal-to-noise ratio (SNR). Here we introduce a robust, efficient machine learning (ML) framework for real-time ODMR analysis based on a one-dimensional convolutional neural network (1D-CNN). The model performs direct parameter inference without initial guesses or iterative optimization, and is naturally parallelizable on graphics processing units (GPU) for high-throughput processing. We validate the approach on both synthetic and experimental datasets, showing improved throughput, accuracy and robustness than standard nonlinear fitting, with the largest gains in the low-SNR regime. We further validate our methods in two representative sensing applications: diagnosing intracellular temperature changes using nanodiamond probes and widefield magnetic imaging of superconducting vortices in a high-temperature superconductor. This deep-learning inference framework enables fast and reliable extraction of physical parameters from complex ODMR data and provides a scalable route to real-time quantum sensing and imaging.
Abstract Magnetic field sensors based on quantum sensing effects demonstrate significantly superior sensitivity compared to traditional sensors. Among these, diamond NV centers have emerged as the most promising quantum magnetic field sensors for practical industrial implementation at room temperature. However, current applications of diamond NV centers in power measurement face challenges due to insufficient measurement response speed. To address this challenge, this paper designs a rapid diamond NV-center magnetic field measurement system based on fast demodulation and ultrafast frequency agility. To this end, this paper employs a field-programmable gate array (FPGA) board-level device to implement signal demodulation and microwave control, thereby enabling a rapid magnetic field measurement system for diamond NV centers with fast demodulation and ultrafast frequency switching. By outputting current through a current source to a double-layer Helmholtz coil, the system generated a z-axis magnetic field. The system achieved a current measurement accuracy of 0.08% within the range of 0.5–10 A, with a static magnetic field sensitivity exceeding 8.30 nT/Hz 1 / 2 . The designed system achieves signal demodulation within less than 80 μs and frequency hopping of modulated microwave signals within less than 1 μs. Compared to benchtop systems, the measurement response speed has been improved from the second-level timescale to the millisecond-level timescale.
The integration of light and materials technology is key to the creation of innovative sensing technologies. Sensing of electric and magnetic fields, and temperature with high spatio-temporal resolution is a critical task for the development of the next-generation of nanometer-scale quantum devices. Color centers in diamonds are attractive for potential applications owing to their characteristic quantum states, although they require metallic contacts for the introduction of external microwaves. Here, we build an ultrafast diamond nonlinear photonic sensor to assess the surface electric field; an electro-optic sensor based on nitrogen-vacancy centers in a diamond nanotip breaks the spatial-limit of conventional pump-probe techniques. The 10-fs near-infrared optical pulse modulates the surface electric field of a 2D transition metal dichalcogenide and we monitor the dynamics of the local electric field at nanometer-femtosecond spatio-temporal resolutions. Our nanoscopic technique will provide new horizons to the sensing of advanced nano materials.
We presented a high-sensitivity temperature detection using an implanted single nitrogen-vacancy (NV) center array in diamond. The high-order thermal Carr-Purcell-Meiboom-Gill (TCPMG) method was performed on the implanted single NV center in diamond in a static magnetic field. We demonstrated that under small detunings for the two driving microwave frequencies, the oscillation frequency of the induced fluorescence of the NV center equals approximately the average of the detunings of the two driving fields. On the basis of the conclusion, the zero-field splitting D for the NV center and the corresponding temperature could be determined. The experiment showed that the coherence time for the high-order TCPMG was effectively extended, particularly up to 108 $\ensuremath{\mu}\mathrm{s}$ for TCPMG-8, about 14 times the value 7.7 $\ensuremath{\mu}\mathrm{s}$ for thermal Ramsey method. This coherence time corresponded to a thermal sensitivity of 10.1 mK/Hz1/2. We also detected the temperature distribution on the surface of a diamond chip in three different circumstances by using the implanted NV center array with the TCPMG-3 method. The experiment implies the feasibility of using implanted NV centers in high-quality diamonds to detect temperatures in biology, chemistry, materials science, and microelectronic systems with high sensitivity and nanoscale resolution.
Quantum sensors encompass a diverse class of devices that exploit quantum coherence to detect weak or nanoscale signals. As their behavior is tied to physical constants, quantum sensors can achieve accuracy, repeatability, and precision approaching fundamental limits [1, 2]. As a result, these sensors have shown utility in a wide range of applications in both science and engineering leading to innovations. Among many solid-state spin systems, nitrogen-vacancy (NV) centers in diamonds have attracted considerable attention as a leading platform in many fields of quantum technologies, from quantum sensing to information processing and communication applications. They have superior physical properties, and their quantum coherence is preserved even at room temperature. The energy levels of NV centers are sensitive to magnetic fields, electric fields, strain, and temperature, and the spatial resolution is scalable from nanometers to millimeters. Therefore, diamond quantum sensors are distinctive sensors with multimodality and span scalable application fields. To realize an ideal quantum sensor for applications, the technical challenges in the sensor materials and the quantum control techniques, such as coherent microwave spin manipulation, effective optical spin-state initialization, and readout, have been developed. This chapter briefly explains the sensing principle using NV centers and then introduces sensor systems for biomedical applications, and monitoring of batteries for electric vehicles and internal power devices.
Modern sensing applications increasingly require the simultaneous measurement of multiple physical parameters with high sensitivity. However, traditional sensors often suffer from cross-sensitivity to different variables, making it difficult to separate overlapping effects, such as those caused by temperature and magnetic fields, in complex environments. This challenge highlights the need for robust multiparametric sensors capable of independently and accurately monitoring diverse conditions in real time. In this context, nitrogen-vacancy (NV) centers in diamond present a promising solution. These atomic-scale defects can be optically initialized, manipulated, and read out via laser excitation using optically detected magnetic resonance (ODMR), and they can operate under ambient and harsh conditions. In this work, we demonstrate a simultaneous and internally cross-validated sensing platform capable of detecting both magnetic fields and temperature with a sensitivity of around 500 nT/Hz and 10 mK/Hz, respectively. Our method integrates ODMR, zero-phonon line spectral shifts, and linewidth broadening, each providing independent spectral signatures sensitive to distinct physical effects. This approach enhances measurement precision, enables internal consistency checks, and decouples intertwined phenomena such as temperature-induced variations in magnetization. The demonstrated technique significantly expands the functional capabilities of NV-based sensors, advancing their potential for high-precision metrology in complex environments.