Machine-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913402537902080 |
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| author | Leger, Polina A. Ramesh, Aditya Ulloa, Talianna Wu, Yingying |
| author_facet | Leger, Polina A. Ramesh, Aditya Ulloa, Talianna Wu, Yingying |
| contents | Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models -- SegNet, 1D U-Net, and 2D U-Net -- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective method for identifying layer thickness in this class of materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials Leger, Polina A. Ramesh, Aditya Ulloa, Talianna Wu, Yingying Materials Science Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models -- SegNet, 1D U-Net, and 2D U-Net -- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective method for identifying layer thickness in this class of materials. |
| title | Machine-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2406.16211 |