The paper introduces a spatial-frequency learning framework for enhancing Scanning Transmission Electron Microscopy (STEM) images. It employs a spatial-frequency fusion network that separately enhances features in the spatial and frequency domains before fusing them, leveraging periodic atomic arrangements in the frequency domain and local atomic details in the spatial domain. Additionally, the authors present a STEM noise calibration method to synthesize realistic images, creating a diverse dataset that improves performance on synthetic and real advanced material images, including better atomic localization accuracy.
By Hesong Li, Ying Fu, Ziqi Wu, Ruiwen Shao, Tao Zhang
arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.
By Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh
arXiv:2609.36639v1 Announce Type: new
Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in disco...
By Huanhuan Zhao, Laxmi Bhurtel, Connor Vernachio, Fahmy Paiziah, Wonhee Ko, Arpan Biswas
arXiv:2606.24817v3 Announce Type: replace
Abstract: Advanced semiconductor nodes drastically increased demand for Transmission Electron Microscopy (TEM), yet destructive sample preparation, slow imag...
By Johannes Boehm, Bappaditya Dey
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.
The paper introduces a ptychography‑supervised learning framework that transforms 4D‑STEM data into real‑time atomic‑resolution phase images. By training a compact model on physics‑constrained reference phase maps from a single AuPd dataset, the method predicts local phase patches directly from diffraction patterns without probe calibration or iterative optimization. The resulting workflow achieves an online latency of ~0.27 ms per probe position, a 1,000‑fold speed‑up over GPU‑accelerated ePIE, and maintains atomic‑scale lattice contrast while generalizing across materials, defocus conditions, and instruments.
By H. Yue, C. -C. Chen, C. -N. Hsiao, J. Cheng, Y. Liu, X. Z. Liao, Steve F. Shu
arXiv:2607. 10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization.
By Evropi Toulkeridou, Jiafei Li, Leonardo Lari, Panagiotis Grammatikopoulos
arXiv:2609.21593v1 Announce Type: new
Abstract: Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yie...
By Yuyan Guan, Haoran Zhang, Zian Mao, Antong Yang, Caifei Li, Jialong Wang, Chuying Ouyang, Hong Wang, Xiaoqin Zeng, Yujun Xie
arXiv:2505. 12650v2 Announce Type: replace-cross Abstract: Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity.
By Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen, Jiangjie Qiu, Hao Xiong, Haoyu Yin, Zhiyao Luo, Yifei Zhang, Sijia Tao, Wentao Li, Qinghua Zhang, Yuqiang Li, Wanli Ouyang, Bin Zhao, Xiaonan Wang, Fei Wei
arXiv:2506. 01678v2 Announce Type: replace-cross Abstract: Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules.
By Nikola L. Kolev, Max Trouton, Filippo Federici Canova, Geoff Thornton, David Z. Gao, Neil J. Curson, Taylor J. Z. Stock
arXiv:2610.33580v1 Announce Type: new
Abstract: Serial electron microscopy (EM) is essential for large-volume biological imaging and connectomics, yet its throughput remains a major bottleneck, with...
By Jiateng Shou, Zhiwei Xiong, Ruikang Xu, Zhicheng Wang, Peiyao Shi, Te Shi, Haiqun Jin, Chunying Yin, Jun Guo, Manfred Auer, Hanchuan Peng, Ruobing Zhang, Feng Wu