Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms

The number of layers of 2D materials is of great significance for regulating the performance of nanoelectronic devices and optoelectronic devices, where the optimal thickness of the target sample should be determined before further physical research or device manufacturing steps. At present, a variety of different optical technologies have been proposed to determine the thickness of samples by using the relationship between the number of layers and optical properties, including optical contrast, optical imaging, Raman spectra, photoluminescence spectra, nonlinear spectra, near-field optical imaging, ellipsometry spectra, and hyperspectral imaging. In the past decade, the rapidly growing number of 2D materials and their heterostructures has exceeded the capacity of traditional experimental and computational methods. In recent years, machine learning (ML) is emerging as a powerful tool to support such traditional methods, which brings new opportunities to tap the potential of optical technology in a more perspicacious way. The application of optical technology has greatly facilitated the acquisition of optical information of materials, while ML algorithms provide a fast, high-throughput, and intelligent way to complete back-end data processing and inference. A profound integration of conventional optical technologies and ML algorithms significantly helps 2D materials progress in basic studies and practical applications and further promotes industrial manufacturing.