Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00470 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915178464935936 |
|---|---|
| author | He, Junqi Zhang, Yujie Wang, Jialu Wang, Tao Zhang, Pan Cai, Chengjie Yang, Jinxing Lin, Xiao Yang, Xiaohui |
| author_facet | He, Junqi Zhang, Yujie Wang, Jialu Wang, Tao Zhang, Pan Cai, Chengjie Yang, Jinxing Lin, Xiao Yang, Xiaohui |
| contents | Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods. Leveraging advancements in artificial intelligence, we introduce a deep learning-based method for efficiently characterizing heterostructures and 2D materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes with varying shapes and thicknesses. By utilizing YOLO models, we achieve an accuracy rate of over 94.67% in identifying these materials. Additionally, we explore the application of transfer learning across different materials, which further enhances model performance. This model exhibits robust generalization and anti-interference ability, ensuring reliable results in diverse scenarios. To facilitate practical use, we have developed an application that enables real-time analysis directly from optical microscope images, making the process significantly faster and more cost-effective than traditional methods. This deep learning-driven approach represents a promising tool for the rapid and accurate characterization of 2D materials, opening new avenues for research and development in material science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning He, Junqi Zhang, Yujie Wang, Jialu Wang, Tao Zhang, Pan Cai, Chengjie Yang, Jinxing Lin, Xiao Yang, Xiaohui Machine Learning Materials Science Computer Vision and Pattern Recognition Optics Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods. Leveraging advancements in artificial intelligence, we introduce a deep learning-based method for efficiently characterizing heterostructures and 2D materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes with varying shapes and thicknesses. By utilizing YOLO models, we achieve an accuracy rate of over 94.67% in identifying these materials. Additionally, we explore the application of transfer learning across different materials, which further enhances model performance. This model exhibits robust generalization and anti-interference ability, ensuring reliable results in diverse scenarios. To facilitate practical use, we have developed an application that enables real-time analysis directly from optical microscope images, making the process significantly faster and more cost-effective than traditional methods. This deep learning-driven approach represents a promising tool for the rapid and accurate characterization of 2D materials, opening new avenues for research and development in material science. |
| title | Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning |
| topic | Machine Learning Materials Science Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2503.00470 |