Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning

• URL: http://arxiv.org/abs/2503.00470v1
• Date: Sat, 01 Mar 2025 12:51:32 GMT
• Title: Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning
• Authors: Junqi He, Yujie Zhang, Jialu Wang, Tao Wang, Pan Zhang, Chengjie Cai, Jinxing Yang, Xiao Lin, Xiaohui Yang,
• Abstract summary: We introduce a deep learning-based method for characterizing 2D materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes with varying shapes and thicknesses.<n>By utilizing YOLO models, we achieve an accuracy rate of over 94.67% in identifying these materials.
• Score: 23.746126635186503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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