{"ID":2893678,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13527","arxiv_id":"2507.13527","title":"SparseC-AFM: a deep learning method for fast and accurate characterization of MoS$_2$ with C-AFM","abstract":"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.","short_abstract":"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 speed...","url_abs":"https://arxiv.org/abs/2507.13527","url_pdf":"https://arxiv.org/pdf/2507.13527v1","authors":"[\"Levi Harris\",\"Md Jayed Hossain\",\"Mufan Qiu\",\"Ruichen Zhang\",\"Pingchuan Ma\",\"Tianlong Chen\",\"Jiaqi Gu\",\"Seth Ariel Tongay\",\"Umberto Celano\"]","published":"2025-07-17T20:38:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cond-mat.mtrl-sci\"]","methods":"[]","has_code":false}