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

TL;DR

SparseC-AFM leverages deep learning to rapidly reconstruct conductivity maps of 2D materials like MoS$_2$ from sparse C-AFM scans, significantly reducing data acquisition time while maintaining accuracy, thus advancing industrial-scale 2D material characterization.

Contribution

This work introduces a novel deep learning approach that enables fast, accurate conductivity mapping of 2D materials from sparse C-AFM data, outperforming traditional methods in speed and efficiency.

Findings

Achieves over 11x reduction in acquisition time.

Maintains electrical property accuracy comparable to full-resolution data.

Robust across various scanning modes and experimental conditions.

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.