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

TL;DR

This paper introduces a new spectral fitting method called dip-peak fitting for ensemble NV center ODMR spectra, significantly improving temperature sensing precision by more accurately modeling spectral features near resonance.

Contribution

The authors develop and validate a physically motivated dip-peak fitting model that outperforms traditional Lorentzian and Voigt models in ensemble NV ODMR spectral analysis.

Findings

Dip-peak fitting yields more accurate resonance frequency extraction.

The method enables faster and more precise temperature measurements.

Experimental results confirm robustness in nanodiamond ensembles.

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.