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arXiv:2605.18863

Enhanced Temperature Sensitivity in Ensemble NV Centers through Improved Optically Detected Magnetic Resonance Spectral Modeling

Yuki S. Kato, Shingo Sotoma, Keisuke Fujita, Masanori Fujiwara, Izuru Ohki, Yuichiro Matsuzaki, Norikazu Mizuochi, Yoshie Harada

physics.ins-detquant-phSubmitted May 15, 2026• Announced May 18, 2026
arXivHTMLPDF

Abstract

Nitrogen-vacancy (NV) center ensembles provide a powerful platform for high-precision temperature sensing, with ongoing efforts to further enhance their measurement performance. In ensemble NV optically detected magnetic resonance (ODMR) spectra, commonly used Lorentzian and Voigt fitting models fail to accurately describe the spectral shape near the resonance frequency, leading to degraded precision in resonance-frequency determination and, consequently, temperature estimation. In this work, we analytically establish a new fitting method, termed dip-peak fitting, for extracting the resonance frequency from ensemble cw-ODMR spectra. Starting from a physical model that describes ensemble cw-ODMR spectra as a convolution of single-NV responses with distributed zero-field splitting and strain, we show that the spectral feature near resonance can be accurately approximated by a single Lorentzian function with a background term. The proposed fitting model reproduces the cw-ODMR spectrum around resonance more faithfully than conventional approaches, enabling faster and more accurate resonance-frequency determination under weaker microwave excitation. Experiments using fluorescent nanodiamond ensembles confirm the robustness and applicability of this method for high-precision temperature sensing.

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Nitrogen-vacancy (NV) center ensembles provide a powerful platform for high-precision temperature sensing, with ongoing efforts to further enhance their measurement performance. In ensemble NV optically detected magnetic resonance (ODMR) spectra, commonly used Lorentzian and Voigt fitting models fail to accurately describe the spectral shape near the resonance frequency, leading to degraded precision in resonance-frequency determination and, consequently, temperature estimation. In this work, we analytically establish a new fitting method, termed dip-peak fitting, for extracting the resonance frequency from ensemble cw-ODMR spectra. Starting from a physical model that describes ensemble cw-ODMR spectra as a convolution of single-NV responses with distributed zero-field splitting and strain, we show that the spectral feature near resonance can be accurately approximated by a single Lorentzian function with a background term. The proposed fitting model reproduces the cw-ODMR spectrum around resonance more faithfully than conventional approaches, enabling faster and more accurate resonance-frequency determination under weaker microwave excitation. Experiments using fluorescent nanodiamond ensembles confirm the robustness and applicability of this method for high-precision temperature sensing.
Fiber-coupled ensembles of nanodiamonds containing nitrogen-vacancy (NV) centers are widely used for microscopic magnetic field measurements. However, the random distribution of NV crystal axes in nanodiamond ensembles causes resonance frequencies from differently oriented NV centers to overlap in the ODMR spectrum, distorting the spectral line shape and breaking the linear mapping between frequency shift and magnetic field. As a result, conventional magnetic field measurements based on Lorentzian fitting suffer from significant systematic errors. Here, we establish a fiber-nanodiamond ensemble model to analyze the impact of random NV orientations on the ODMR linear response and define an effective ensemble-averaged linear response coefficient K . Fisher information analysis identifies an information-optimal intrinsic magnetic field of approximately 640 µT, at which the ensemble response can be reliably approximated as linear in its vicinity. Building on this condition, we propose a model-assisted regression scheme, in which the physical fiber-nanodiamond ensemble model defines the optimal operating condition and the associated linear frequency-field scaling, while intrinsic-field-guided Gaussian regression (IFGGR) extracts magnetic-field-induced frequency shifts from distorted ensemble ODMR spectra, enabling accurate magnetic field estimation via the response coefficient K . Under identical acquisition conditions, the proposed method reduces the RMSE from 37.21 µT to 8.99 µT and significantly suppresses systematic bias, while achieving an absolute sensitivity approaching the shot-noise limit at optimal averaging time. The relative slope error reduction (≈15.6-fold) further confirms improved local linearity near the optimal operating point.
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.
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.
Magnetometry with nitrogen-vacancy (NV) centers in diamond has so far been measured via emission of light from NV centers or via absorption at the singlet transition at 1042 nm. Here, we demonstrate a phenomenon of broadband optical absorption by the NV centers starting in the emission wavelength and reaching up to 1000 nm. The measurements are enabled by a high-finesse cavity, which is used for room-temperature continuous wave pump-probe experiments. The red to infrared probe beam shows the typical optically detected magnetic resonance (ODMR) signal of the NV spin with contrasts up to 42 %. This broadband optical absorption has not yet been reported in terms of NV magnetometry. We argue that the lower level of the absorbing transition could be the energetically lower NV singlet state, based on the increased optical absorption for a resonant microwave field and the spectral behavior. Investigations of the photon-shot-noise-limited sensitivity show improvements with increasing probe wavelength, reaching an optimum of 7.5pT/Hz. The results show significantly improved ODMR contrast compared to emission-based magnetometry. This opens a new detection wavelength regime with coherent laser signal detection for high-sensitivity NV magnetometry.
Nitrogen-vacancy (NV) centers in diamond are solid-state quantum sensors that operate at room temperature and allow optical readout of spin states through optically detected magnetic resonance (ODMR). Reliable ODMR measurements need controlled microwave excitation to manipulate NV spin states and modulate their fluorescence. In this paper, we report on ensemble NV centers’ interaction with microwave fields transmitted by printed-circuit-board (PCB) radio-frequency (RF) devices. Two microwave structures, a coplanar waveguide transmission line (CPW-TL) and a single-finger interdigitated capacitor (IDC), are designed and impedance-matched at the NV zero-field splitting frequency. Nanodiamond solutions containing NV ensembles are deposited as few-microliter droplets onto the PCB devices and dried, forming localized NV layers on the RF structures. Two-dimensional fluorescence scans are used to link spatial changes in NV fluorescence to the microwave field distributions created by each device geometry. CPW-TLs enable interaction over extended regions along the transmission line, while IDCs produce localized excitation near the capacitive gaps.
Abstract Optically detected magnetic resonance (ODMR) is a way to characterize the ensemble of NV − centers. Recently, a remarkably sharp dip was observed in the ODMR with a high-density ensemble of NV centers. The model (Zhu et al 2014 Nat. Commun . 5 3424) indicated that such a dip was due to the spin-1 properties of the NV − centers. Here, we present many more details of the analysis to show how this model can be applied to investigate the properties of the NV − centers. By using our model, we have reproduced the ODMR with and without applied external magnetic fields. Additionally, we investigate how the ODMR is affected by the typical parameters of the ensemble NV − centers such as strain distributions, inhomogeneous magnetic fields, and homogeneous broadening width. Our model provides a way to characterize the NV − center from the ODMR, which would be crucial to realize diamond-based quantum information processing.