CHARACTERIZATION OF ARTIFICIALLY GENERATED 2D MATERIALS USING CONVOLUTIONAL NEURAL NETWORKS

Yıl 2022, Cilt: 23 Sayı: 3, 223 - 232, 27.09.2022

Cahit Perkgöz , Mehmet Zahit Angi

https://doi.org/10.18038/estubtda.1149416

Öz

Two dimensional (2D) materials have attracted many researchers due to the high-performance of the devices produced by these materials. There are different methods to produce 2D materials such as wet chemical synthesis, chemical vapor deposition (CVD), molecular beam epitaxy, atomic layer deposition, pulsed laser deposition (PLD), all of which require hours during the processes. Once the 2D structures are obtained, their properties including their defects should be revealed by different characterization tools. Characterization process also requires time and expertise. In this respect, deep learning methods such as Convolutional Neural Networks (CNN) can be a solution for the practical and rapid classification of the produced samples. However, there is not enough number of samples in most of the research laboratories because of the above-mentioned long experimental processes. This work presents the performance of a CNN algorithm using artificially created images of MoS2, a commonly studied 2D semiconductor with a high potential in different electronics applications. The synthetic optical microscopic images including normal and defected MoS2 flakes are generated by the intensities of light incident on different materials using Fresnel Equations. A deep CNN algorithm is constructed to detect the normal and defective samples. As a result of the experiments, an average of 88.9% accuracy was obtained. These results can be interpreted that CNN can be used in the future for the characterization of two-dimensional materials with a sufficient number of real images.

Anahtar Kelimeler

MoS2, Two-dimensional Materials, Fresnel Equations, Characterization, Deep Learning

Destekleyen Kurum

Eskişehir Teknik Üniversitesi

Proje Numarası

22ADP144

Teşekkür

This work was supported by Eskişehir Technical University Research Project no: 22ADP144.

Kaynakça

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