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

Abstract

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.

Backside Absorbing Layer Microscopy: Monolayer Counting in 2D Crystal Flakes

“…[33,34] The same strategy would apply to the combination of our approach with machine learning which can be seen as an empiric alternative to numerical models. [35] Last but not least, all the operations described in our treatment can be implemented as real time image processing tools, allowing the live counting of monolayers in 2D material stacks.…”

Phyllosilicates as earth-abundant layered materials for electronics and optoelectronics: Prospects and challenges in their ultrathin limit

“…Given the increasing amount of theoretical and experimental data, ML methods can be employed to predict many structure-related properties, such as the essential requirement of thermodynamic stability. 98 With experimental data, it can be used for LMs identification with optical microscopy, Raman and photoluminescence (PL) spectroscopies, transmission electron microscopy, hyperspectral imaging, and AFM, [99][100][101][102] including the number of layers identification and real-time analysis. Towards realistic systems…”

When Machine Learning Meets 2D Materials: A Review

“…To our knowledge, there have been seven published reviews related to the current topic, [ 1 , 2 , 118 , 119 , 120 , 121 , 122 ] each covering some specific aspects of it. However, a systematic overview with updated references is highly desirable.…”