AI Insight
This study introduces a new spectral fitting method called "dip-peak fitting" for ensemble nitrogen-vacancy (NV) center optically detected magnetic resonance (ODMR) spectra, which are used in high-precision temperature sensing. The researchers demonstrate analytically that the spectral shape near resonance frequency can be accurately described by a single Lorentzian function with a background term, derived from a physical model accounting for distributed zero-field splitting and strain across the NV ensemble. Experiments with fluorescent nanodiamond ensembles confirm that this approach outperforms conventional Lorentzian and Voigt fitting models in both accuracy and speed of resonance-frequency determination, particularly under weaker microwave excitation conditions.
Why it matters
Improved resonance-frequency extraction directly enhances temperature measurement precision in NV-based sensors, which have potential applications in nanoscale thermometry, biological imaging, and quantum sensing technologies. More accurate and computationally efficient fitting methods could accelerate the practical deployment of diamond-based sensors in medical and materials science contexts.
arXiv:2605.18863v1 Announce Type: new
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.