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

2025-07-17

AI Summary

To address the low efficiency of electrical conductivity characterization for two-dimensional materials (e.g., MoS₂) in large-scale nanoelectronics manufacturing, this work proposes a deep learning–based, sparse-scanning data-driven method for reconstructing high-resolution conductance maps. Moving beyond the time-consuming full-pixel conductive atomic force microscopy (C-AFM) scanning, we develop a generalizable neural network model robust across diverse scanning modalities, substrates, and experimental conditions. Leveraging multi-condition robust training, the model accurately identifies and reconstructs critical microstructural features—including thin-film coverage, defect density, and grain boundary locations. The approach reduces scanning time by ~67% (5 min vs. 15 min) and decreases data acquisition volume by over 11×, while achieving excellent reconstruction fidelity (R² > 0.96 versus ground-truth high-resolution conductance maps). This represents the first scalable, AI-assisted electrical characterization framework bridging laboratory research and industrial deployment.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Fast electrical characterization of 2D materials like MoS2
• Overcoming slow data acquisition in conductive AFM techniques
• Accurate reconstruction of conductivity maps from sparse scans

Innovation

• Deep learning reconstructs conductivity maps from sparse scans
• Reduces C-AFM acquisition time by over 11 times
• Robust across various substrates and scanning modes