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Nitrogen-vacancy centers in diamond have been developed as a sensitive magnetic sensor and broadly applied on condensed matter physics. We present a design of a scanning probe microscope based on a nitrogen-vacancy center that can operate under various experimental conditions, including a broad temperature range (20–500 K) and a high-vacuum condition (1 × 10−7 mbar). The design of a compact and robust scanning head and vacuum chamber system is presented, which ensures system stability while enabling the convenience of equipment operations. By showcasing the temperature control performance and presenting confocal images of a single-layer graphene and a diamond probe, along with images of a ferromagnetic strip and an epitaxial BiFeO3 film on the SrTiO3 substrate, we demonstrate the reliability of the instrument. Our study proposes a method and a corresponding design for this microscope that extends its potential applications in nanomagnetism and spintronics. [ABSTRACT FROM AUTHOR]

In the present study, the machine learning algorithm is utilized for the first time to improve the probe diagnosis. Machine learning methods are utilized to improve the Langmuir probe diagnostic accuracy and the diagnosable plasma parameter range without changing the probe structure based on the Langmuir probe. They provide a new way for experimentally obtaining electron density. A DC glow discharge simulation model and experimental equipment are established. Utilizing the discharge pressure and voltage as independent variables, the simulation and experimental electron densities are collected, the simulation and experimental data are utilized for training, and the plasma electron density outside of the pressure and voltage range of the training data is predicted, thereby achieving the prediction. Simultaneously, when the data amount is large enough, even without experimental measurement, the electron density can be obtained directly through the input parameters, without relying on the plasma physical model. [ABSTRACT FROM AUTHOR]