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.
@article{arxiv.2503.00470,
title = {Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning},
author = {Junqi He and Yujie Zhang and Jialu Wang and Tao Wang and Pan Zhang and Chengjie Cai and Jinxing Yang and Xiao Lin and Xiaohui Yang},
journal= {arXiv preprint arXiv:2503.00470},
year = {2025}
}