Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning
This provides a faster and more cost-effective tool for material science researchers, though it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackled the problem of efficiently characterizing two-dimensional materials and heterostructures by introducing a deep learning-based method using YOLO models, achieving over 94.67% accuracy in identification and enabling real-time analysis from optical microscope images.
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.