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title SparseC-AFM
emoji 🔬
colorFrom blue
colorTo purple
sdk gradio
sdk_version 4.44.0
app_file app.py
pinned false

SparseC-AFM: fast 2D-material acquisition & analysis with super resolution models

arXiv Hugging Face Spaces

This is the official Pytorch implementation of our paper: SparseC-AFM: a deep learning method for fast and accurate characterization of MoS2 with C-AFM. We present a novel method for rapid acquisition and analysis of C-AFM scans using a super-resolution model based on the work of SwinIR. In this repository, you can find the datasets and model weights used in our paper, as well as scripts to train and deploy our model on your own datasets.

Getting Started

  • Install uv and run:
uv sync
uv run python app.py

demo/demo.png

Datasets

Path Material Height Maps Current Maps Substrate Mode # Samples # Data Points Resolutions
data/raw-data/3-12-25 BTO ✅ ❌ --- Tapping (AFM Only) 4 16 {64, 128, 256, 512}
data/raw-data/2-6-25 MoS2 ✅ ✅ SiO2-Si Contact 1 4 {64, 128, 256, 512}
data/raw-data/1-23-25 MoS2 ✅ ✅ SiO2-Si Contact 1 5 {512}
data/raw-data/11-19-24 MoS2 ✅ ✅ SiO2-Si, Sapphire Contact 2 10 {512}

Model Weights

Path Upscaling Factor Material Height Maps Current Maps Substrates
data/weights/...2x.pth $$\times2$$ MoS2 ✅ ✅ SiO2-Si, Sapphire
data/weights/...4x.pth $$\times4$$ MoS2 ✅ ✅ SiO2-Si, Sapphire
data/weights/...8x.pth $$\times8$$ MoS2 ✅ ✅ SiO2-Si, Sapphire

Citation

@inproceedings{10.1117/12.3067427,
    author = {Levi Harris and Md Jayed Hossain and Mufan Qui and Ruichen Zhang and Pingchuan Ma and Tianlong Chen and Jiaqi Gu and Seth Ariel Tongay and Umberto Celano},
    title = {{Sparse C-AFM: a deep learning method for fast and accurate characterization of MoS2 with conductive atomic force microscopy}},
    volume = {13582},
    booktitle = {Low-Dimensional Materials and Devices 2025},
    editor = {Nobuhiko P. Kobayashi and A. Alec Talin and Albert V. Davydov and M. Saif Islam},
    organization = {International Society for Optics and Photonics},
    publisher = {SPIE},
    pages = {135820J},
    keywords = {2D materials, MoS2, conductive atomic force microscopy (C-AFM), AFM, Deep Learning },
    year = {2025},
    doi = {10.1117/12.3067427},
    URL = {https://doi.org/10.1117/12.3067427}
}

License

We release our work under the Apache License 2.0 ❤️