Deep-Learning-Enhanced Single-Spin Readout in Silicon Carbide at Room Temperature

Liao, Yu-Wei; Li, Qiang; Yang, Ming; Liu, Zhenghao; Yan, Fei; Wang, Junfeng; Zhou, Jing; Lin, Wenwen; Tang, Yidan; Xu, Jin‐Shi; Li, Chuan‐Feng; Guo, Guang‐Can

doi:10.1103/physrevapplied.17.034046

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…The effect of the magnetic-angle-dependent coherence time of divacancy has also been investigated, which in turn can be used as the magnetic angle sensing using the ODMR spectra [33]. Moreover, it has been shown that a well-trained convolutional neural network can precisely predict the resonance frequency of the ODMR spectra and the period of the Rabi oscillations with a low signal-to-noise ratio [157]. The method assisted by machine learning is less time-consuming and is helpful for real-time magnetometry.…”

Section: Silicon Vacancymentioning

confidence: 99%

Quantum systems in silicon carbide for sensing applications

Castelletto,

Lew,

Lin

et al. 2023

Rep. Prog. Phys.

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This paper summarizes recent studies identifying key qubit systems in silicon carbide (SiC) for quantum sensing of magnetic, electric fields, and temperature at the nano and microscale. The properties of colour centres in SiC, that can be used for quantum sensing, are reviewed with a focus on paramagnetic colour centres and their spin Hamiltonians describing Zeeman splitting, Stark effect, and hyperfine interactions. These properties are then mapped onto various methods for their initialization, control, and read-out. We then summarised methods used for a spin and charge state control in various colour centres in SiC. These properties and methods are then described in the context of quantum sensing applications in magnetometry, thermometry, and electrometry. Current state-of-the art sensitivities are compiled and approaches to enhance the sensitivity are proposed. The large variety of methods for control and read-out, combined with the ability to scale this material in integrated photonics chips operating in harsh environments, places SiC at the forefront of future quantum sensing technology based on semiconductors.

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Section: Silicon Vacancymentioning

confidence: 99%

Quantum systems in silicon carbide for sensing applications

Castelletto,

Lew,

Lin

et al. 2023

Rep. Prog. Phys.

Self Cite

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“…In [ 20 ], Tsukamoto et al applied Gaussian process regression for randomly oriented NV ensembles in nano-diamonds, reaching high accuracy of

for fields of

. In the context of a single-spin readout at room temperature, where the signal is typically noisy and thus hard to process, neural networks have recently been proposed to extract the position of resonance peaks in ODMR spectra [ 21 ]. In addition, machine learning has been shown to drastically speed up the measuring process in ODMR magnetometry by using Bayesian experiment design instead of a conventional frequency-swept measurement [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond

Homrighausen

Horsthemke

Pogorzelski

et al. 2023

Sensors

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Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.

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Deep-Learning-Enhanced Single-Spin Readout in Silicon Carbide at Room Temperature

Cited by 2 publications

References 36 publications

Quantum systems in silicon carbide for sensing applications

Quantum systems in silicon carbide for sensing applications

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond

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