Advancing semantic segmentation of two-dimensional materials using a semantic-adaptive transformer model

Abstract

Accurate detection and characterization of two-dimensional (2D) materials are essential for their effective utilization in various applications. Traditional techniques, such as chemical vapor deposition, often produce materials with high defect density, while mechanical exfoliation is hindered by its labor-intensive and time-consuming nature. In this Letter, we propose a semantic-adaptive transformer model, termed Semptive, designed specifically for the precise detection of monolayer MoS2. Our approach integrates a semantic adaptation module with a multi-head self-attention mechanism, incorporating deep supervision and leveraging prior knowledge to enhance model performance. The model was trained on a dataset obtained through mechanical exfoliation and validated using photoluminescence spectroscopy. The experimental results reveal that Semptive significantly enhances segmentation performance compared to conventional models, achieving higher Intersection over Union and Dice scores while reducing computational demands. This method represents a notable advancement in the efficient and precise identification of 2D materials, providing substantial improvements for material characterization and device fabrication processes.