Quantum Noise Spectroscopy of Nanoscale Charge Defects in Silicon Carbide at Room Temperature

Abstract

The nanoscale charge environment critically influences semiconductor physics and device performance. While conventional bulk characterization techniques provide volume-averaged defect properties, they lack the spatial resolution to resolve nanoscale charge heterogeneity and identify microscopic noise sources. Here, we utilize single PL5 centers in 4H-SiC as room-temperature broadband quantum sensors to fill in the gap. We report the first real-time, nanoscale observation of singlecharge tunneling dynamics in a commercial semiconductor at room temperature, by monitoring the random telegraph noise using optically detected magnetic resonance (ODMR). This capability enables an electrical noise imaging technique, showing distinct noise variations across different wafer substrates. By employing dynamical decoupling, we extend noise spectroscopy from near-DC to MHz frequencies, uncovering significant noise spectral density correlations across frequency bands. Finally, we probe MHz-GHz noise and identify its origin via T1 relaxation spectroscopy, obtaining the first nanoscale electron paramagnetic resonance (EPR) spectroscopic fingerprint of charge defects in SiC. These techniques open avenues for characterizing noise environments in semiconductor devices, providing critical insights for optimizing SiC fabrication processes, defect control, and advancing quantum technologies.

Figures (4)

• Figure 1: Experimental setup and sensing scheme overview. (a) Experiment principle of quantum sensor in silicon carbide. A 905nm laser is used to realize the initialization and readout of PL5 center. Electrical and magnetic noise in semiconductors can have a significant impact on the performance of high-power electronic devices as well as on the coherence properties of qubits embedded in solid-state materials. An external magnetic field parallel to the principal axis of PL5 center is applied to tune the energy levels when needed. The intrinsic none-zero term $E_\perp$ for PL5 makes the transition between ground states $\{\ket{0},\ket{+}\}$ exclusively sensitive to transverse electric noise under zero magnetic field. When $B_z$ is applied, the transition become sensitive to longitudinal magnetic noise. (b) Physical properties of the local charge environment. The panels illustrate three key dimensions of defect characterization: (Left) Charge hopping, showing the time-domain random telegraph noise induced by discrete charge trapping and releasing events; (Middle) Power Density, displaying the frequency-domain noise spectrum for both magnetic (blue) and electrical (green) noise components; and (Right) Defect Species, revealing the spectroscopic fingerprint used to identify specific defects. (c) Corresponding quantum sensing protocols. The methods span from DC to GHz frequencies: (Left) Real-time tracking of ODMR resonance shifts $\Delta f$ monitor low-frequency fluctuations; (Middle) Dynamical decoupling sequences (e.g., XY8) with variable inter-pulse spacing to probe noise power density in the MHz regime; and (Right) $T_1$ relaxometry-based EPR spectroscopy to probe charge defects species in the GHz regime.
• Figure 2: Nanoscale monitoring of electric environment at single charge level. (a) Four typical CW spectra configurations with different central frequency of a single quantum sensor were observed, which were labeled as A-D. (b) Time-resolved CW-ODMR measurements on a single PL5 center reveal the evolution of its spectral peak position. (c) Histogram illustrates the distribution of peak positions in (b). Four well-defined stable states can be clearly identified by fitting, corresponding to the four states identified in panel (a). The peak separation between State-A and C (B and D) is $3\pm 0.3$ MHz, while that between State-A and B (C and D)is $1\pm 0.3$ MHz. This indicates that the observed spectral fluctuations result from a composition of two two-level system (TLS) signals. (d) The possible location of the two charge traps with 95 % confidence interval based on the splitting of different states shown in (c). The detail of the calculation can be found in SM.
• Figure 3: Electrical noise imaging of samples with different wafers. (a-b) CW-ODMR tracking data of a single PL5 center in sample from wafer 1. Standard deviation of the ODMR peak position $\sigma_f$ is used to quantify the instability and the effective amplitude of ambient electrical noise. Right panel shows noise strength ($\sigma_f$) spatial distribution obtained by two-dimensional interpolation. (c-d) Same analysis in sample from wafer 2, which was diced from another manufacturer. Both wafers feature intrinsic epitaxial layers grown on n-type substrates.
• Figure 4: Noise strength correlation and spectrum of PL5 centers with EPR identification of noise source. (a) Hahn echo $T_2$ measurement of the two PL5 centers (named PL5-A and PL5-B) on wafer 1. The sensor with lower near-DC noise (PL5-A) exhibits a longer coherence time. (b) A rapid decrease in Hahn echo coherence time is observed as the spectral fluctuation ($\sigma_f$) increases, indicating a strong correlation between near-DC noise and high-frequency electrical noise. The dashed cureve serves as a fitting guideline with $T_2\propto 1/\sigma^k$. (c) Noise spectra obtained from XY8 dynamical decoupling measurements on PL5-A and PL5-B. Experiments were performed under both zero field and 120G magnetic field aligned along the defect axis. (d) SQ and DQ transition rates are extracted using different spin state initialization and readout schemes. Right pannel shows the measurement result of PL5-B at 260.4G along PL5 axis. Fitting model follows the equation shown in Eq.\ref{['relaxation']}. (e) Comparison of electrical noise strength $\gamma$ between the two PL5 centers, showing that the unstable sensor exhibits a stronger 1/$f$-type noise. (f) Transition energy ($\Delta$) for PL5 center and V2 center are plotted against magnetic field. (g) $T_1$ relaxometry for two PL5 centers. Here, relaxation rate $\Gamma$ was extracted from $S_1-S_3$ shown in (d). The observed accelerated relaxation of PL5-B reveals the presence of environmental noise. Based on the extracted spectral peak position and calculation of transition energy shown in (f), we attribute this signal to nearby silicon vacancy (V2) and other spin-1/2 traps, while no signal was observed for PL5-A. Errorbar is given by 1 SD.