标题： SparseC-AFM：一种用于快速准确表征MoS$_2$的深度学习方法，结合C-AFM 显示英文标题

标题： SparseC-AFM: a deep learning method for fast and accurate characterization of MoS$_2$ with C-AFM

摘要： 随着二维（2D）材料在纳米电子学中的广泛应用，对电气特性的稳健计量技术的需求日益增加，尤其是在大规模生产中。 虽然原子力显微镜（AFM）技术如导电AFM（C-AFM）具有高精度，但由于光栅扫描过程，它们的数据采集速度较慢。 为了解决这个问题，我们引入了SparseC-AFM，这是一种深度学习模型，可以快速且准确地从稀疏的C-AFM扫描中重建如MoS$_2$的二维材料的电导率图。 我们的方法在各种扫描模式、基底和实验条件下都具有鲁棒性。 我们报告了（a）经典流程实现的比较，其中通过手动解析高像素密度的C-AFM图像（例如，收集需要15分钟）来提取相关的材料参数，以及（b）我们的SparseC-AFM方法，该方法使用所需采集时间显著减少的数据（例如，不到5分钟）实现了相同的操作。 SparseC-AFM能够高效提取MoS$_2$的关键材料参数，包括薄膜覆盖率、缺陷密度以及晶体岛边界、边缘和裂纹的识别。 与从全分辨率C-AFM图像进行手动提取相比，我们实现了超过11倍的采集时间减少。 此外，我们证明了模型预测的样品表现出与经典流程扫描获得的全分辨率数据非常相似的电气特性。 这项工作标志着将人工智能辅助的二维材料表征从实验室研究向工业制造迈出的重要一步。 代码和模型权重可在github.com/UNITES-Lab/sparse-cafm获取。 显示英文摘要

摘要： The increasing use of two-dimensional (2D) materials in nanoelectronics demands robust metrology techniques for electrical characterization, especially for large-scale production. While atomic force microscopy (AFM) techniques like conductive AFM (C-AFM) offer high accuracy, they suffer from slow data acquisition speeds due to the raster scanning process. To address this, we introduce SparseC-AFM, a deep learning model that rapidly and accurately reconstructs conductivity maps of 2D materials like MoS$_2$ from sparse C-AFM scans. Our approach is robust across various scanning modes, substrates, and experimental conditions. We report a comparison between (a) classic flow implementation, where a high pixel density C-AFM image (e.g., 15 minutes to collect) is manually parsed to extract relevant material parameters, and (b) our SparseC-AFM method, which achieves the same operation using data that requires substantially less acquisition time (e.g., under 5 minutes). SparseC-AFM enables efficient extraction of critical material parameters in MoS$_2$, including film coverage, defect density, and identification of crystalline island boundaries, edges, and cracks. We achieve over 11x reduction in acquisition time compared to manual extraction from a full-resolution C-AFM image. Moreover, we demonstrate that our model-predicted samples exhibit remarkably similar electrical properties to full-resolution data gathered using classic-flow scanning. This work represents a significant step toward translating AI-assisted 2D material characterization from laboratory research to industrial fabrication. Code and model weights are available at github.com/UNITES-Lab/sparse-cafm.