Highly efficient computer algorithm for identifying layer thickness of atomically thin 2D materials

Abstract

The fields of layered material research, such as transition-metal dichalcogenides (TMDs), have demonstrated that the optical, electrical and mechanical properties strongly depend on the layer number N. Thus, efficient and accurate determination of N is the most crucial step before the associated device fabrication. An existing experimental technique using an optical microscope is the most widely used one to identify N. However, a critical drawback of this approach is that it relies on extensive laboratory experiences to estimate N; it requires a very time-consuming image-searching task assisted by human eyes and secondary measurements such as atomic force microscopy and Raman spectroscopy, which are necessary to ensure N. In this work, we introduce a computer algorithm based on the image analysis of a quantized optical contrast. We show that our algorithm can apply to a wide variety of layered materials, including graphene, MoS 2 , and WS 2 regardless of substrates. The algorithm largely consists of two parts. First, it sets up an appropriate boundary between target flakes and substrate. Second, to compute N, it automatically calculates the optical contrast using an adaptive RGB estimation process between each target, which results in a matrix with different integer Ns and returns a matrix map of Ns onto the target flake position. Using a conventional desktop computational power, the time taken to display the final N matrix was 1.8 s on average for the image size of 1280 pixels by 960 pixels and obtained a high accuracy of 90% (six estimation errors among 62 samples) when compared to the other methods. To show the effectiveness of our algorithm, we also apply it to TMD flakes transferred on optically transparent c-axis sapphire substrates and obtain a similar result of the accuracy of 94% (two estimation errors among 34 samples).

Large area chemical vapour deposition grown transition metal dichalcogenide monolayers automatically characterized through photoluminescence imaging

“…Computer vision has been used in the field of 2D materials, predominantly as a tool for automatic mono-and multilayer identification of exfoliated material across large areas 43,44 freeing up time taken searching for flakes. The technique of PL imaging has been developed in parallel, as a fast and innovative type of optical imaging, that can be simply applied to a standard optical microscope for wide-field and fast capture of fluorescent monolayer material 45 .…”

A Researcher’s Perspective on Unconventional Lab-to-Fab for 2D Semiconductor Devices

“…It is quite handy that the relative color contrast of 2D TMD flakes and beyond can be directly correlated to the number of layers . With recent advances in machine learning, simple algorithms have been developed to identify layer thickness efficiently . Lattice spacing of the 2D materials is typically measured by using X-ray scattering, diffraction, and reflectivity.…”

“…109 With recent advances in machine learning, simple algorithms have been developed to identify layer thickness efficiently. 110 Lattice spacing of the 2D materials is typically measured by using X-ray scattering, diffraction, and reflectivity. Grazing incidence X-ray diffraction (GIXD) can throw light on the in-plane arrangement of atoms, as has been observed in the case of graphene.…”

A facile method for precise layer number identification of two-dimensional materials through optical images

“…To achieve more precise results, each of the RGB channels of an image is supposed to take into account. So far, color differences in CIELab color space [13], sRGB color space [14][15][16][17] and CIE XYZ color space [18] have been calculated to quantitatively analyze layer numbers of 2D materials.…”