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

• URL: http://arxiv.org/abs/2512.22521v1
• Date: Sat, 27 Dec 2025 08:40:12 GMT
• Title: Quantum Noise Spectroscopy of Nanoscale Charge Defects in Silicon Carbide at Room Temperature
• Authors: Jinpeng Liu, Yuanhong Teng, Yu Chen, Yixuan Wang, Chihang Luo, Jun Yin, Hao Li, Lixing You, Ya Wang, Qi Zhang, Fazhan Shi,
• Abstract summary: We report the first real-time nanoscale observation of singlecharge tunneling dynamics in a commercial semiconductor at room temperature.<n>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.
• Score: 18.992436447163225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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