A novel method to determine the layer number of 2D TMD materials based on optical microscopy and image processing

Abstract Two-dimensional (2D) transition metal dicakcoganite (TMD) materials have unique electronic and optical properties. The electronic band structure of TMDs alter as a function of layer numbers, which results in modifications of their characteristic properties. Therefore, determination of the layer number is crucial for optoelectronic applications. In this study, a fast and easily applicable method is proposed for determining the layer number of two-dimensional TMD materials by using an optical microscopy and computational methods. The method uses image processing techniques on digital images taken with a Complementary Metal Oxide Semiconductor (CMOS) camera under a conventional reflecting microscope. The chromaticity and lightness differences in International Commission on Illumination (Commission internationale de l’éclairage, CIE) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">L</mml:mi> </mml:mrow> <mml:mo>*</mml:mo> </mml:msup> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">u</mml:mi> </mml:mrow> <mml:mo>*</mml:mo> </mml:msup> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">v</mml:mi> </mml:mrow> <mml:mo>*</mml:mo> </mml:msup> </mml:math> colour space values between the layered areas on the flakes and substrates are used together to train the model to identify the layer numbers of the materials and corrected via photoluminescence spectroscopy. The random forest clasifier algorithm is applied to predict layer numbers on unknown materials. This approach provides high accuracy under varying microscope configurations such as brightnesses, gain etc and material-substrate combinations. From monolayer (ML) to bulk layer numbers of MoS 2 , WSe 2 and WS 2 flakes are determined with high accuracy by training only a single flake of each having various layer numbers. Compared with traditional methods such as Raman spectroscopy, atomic force microscopy (AFM) and photoluminescence (PL), our method is not only faster but also easier to apply. Moreover, unlike the prominent methods in the literature such as machine learning or clustering-based, it requires only single training for a material/substrate combination and further accelerates the processes.