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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.03775 |
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| _version_ | 1866916913080172544 |
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| author | Liu, Mingyu Mao, Zian Liu, Zhu Zhang, Haoran Guo, Jintao He, Xiaoya Huang, Xi Chu, Shufen Cheng, Chun Ding, Jun Xie, Yujun |
| author_facet | Liu, Mingyu Mao, Zian Liu, Zhu Zhang, Haoran Guo, Jintao He, Xiaoya Huang, Xi Chu, Shufen Cheng, Chun Ding, Jun Xie, Yujun |
| contents | Automated experimentation with real time data analysis in scanning transmission electron microscopy (STEM) often require end-to-end framework. The four-dimensional scanning transmission electron microscopy (4D-STEM) with high-throughput data acquisition has been constrained by the critical bottleneck results from data preprocessing. Pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably corrupt diffraction patterns, systematically biasing quantitative measurements. Yet, conventional correction algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we present 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center correction, and elliptical distortion calibration. The network is trained on large, simulated datasets encompassing a wide range of noise levels, drift magnitudes, and distortion types, enabling it to generalize effectively to experimental data acquired under varying conditions. Quantitative evaluations demonstrate that our pipeline reduces mean squared error by up to 50% during denoising and achieves sub-pixel center localization in the center detection task, with average errors below 0.04 pixels. The outputs are bench-marked against traditional algorithms, highlighting improvements in both noise suppression and restoration of diffraction patterns, thereby facilitating high-throughput, reliable 4D-STEM real-time analysis for automated characterization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 4D-PreNet: A Unified Preprocessing Framework for 4D-STEM Data Analysis Liu, Mingyu Mao, Zian Liu, Zhu Zhang, Haoran Guo, Jintao He, Xiaoya Huang, Xi Chu, Shufen Cheng, Chun Ding, Jun Xie, Yujun Computer Vision and Pattern Recognition Materials Science Artificial Intelligence I.2.10; I.5.1; J.2 Automated experimentation with real time data analysis in scanning transmission electron microscopy (STEM) often require end-to-end framework. The four-dimensional scanning transmission electron microscopy (4D-STEM) with high-throughput data acquisition has been constrained by the critical bottleneck results from data preprocessing. Pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably corrupt diffraction patterns, systematically biasing quantitative measurements. Yet, conventional correction algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we present 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center correction, and elliptical distortion calibration. The network is trained on large, simulated datasets encompassing a wide range of noise levels, drift magnitudes, and distortion types, enabling it to generalize effectively to experimental data acquired under varying conditions. Quantitative evaluations demonstrate that our pipeline reduces mean squared error by up to 50% during denoising and achieves sub-pixel center localization in the center detection task, with average errors below 0.04 pixels. The outputs are bench-marked against traditional algorithms, highlighting improvements in both noise suppression and restoration of diffraction patterns, thereby facilitating high-throughput, reliable 4D-STEM real-time analysis for automated characterization. |
| title | 4D-PreNet: A Unified Preprocessing Framework for 4D-STEM Data Analysis |
| topic | Computer Vision and Pattern Recognition Materials Science Artificial Intelligence I.2.10; I.5.1; J.2 |
| url | https://arxiv.org/abs/2508.03775 |