Authors: Ingyu Yoo | Jong-Ho Lee | M.W. Kim | Yoonsang Tak | Miyoung Kim
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Quantitative Atomic Structure Analysis Using Saliency-Based Clustering in STEM <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d16365e88">The introduction of Cs-corrected microscopes and advancements in computing has significantly enhanced access to atomic-level observations in scanning transmission electron microscopy (STEM). Notably, improvements in data processing methodologies and algorithms have driven new insights into local atomic structures. Deep learning models – such as convolutional neural networks (CNN) and variational autoencoders (VAE) [ <a class="xref-link" href="#r1">1</a>, <a class="xref-link" href="#r2">2</a>] – along with statistical methods like Bayesian optimization and clustering [ <a class="xref-link" href="#r3">3</a>, <a class="xref-link" href="#r4">4</a>, <a class="xref-link" href="#r5">5</a>], have facilitated various applications such as feature extraction, defect identification, and atom counting. A fundamental step in applying these methods to atomically resolved STEM is to measure the coordinates of each atomic column. While CNN-based models (e.g., U-Net and Autoencoder) and blob detection model are effective in extracting coordinates [ <a class="xref-link" href="#r1">1</a>, <a class="xref-link" href="#r2">2</a>, <a class="xref-link" href="#r3">3</a>], there remains a need for faster and more widely accessible coordinate extraction method. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d16365e115">In this study, we developed a model that combines a saliency model with a clustering algorithm to enable faster and more reliable operation across diverse structures and compositions in STEM. ( <a class="xref-link" href="#fg001">Fig. 1</a>). Rather than relying solely on CNN-based methods like U-Net or Autoencoders, we adopted a Pyramid Saliency Model. This approach utilizes both low- and high-level features learned by the CNN as inputs to an attention model, enabling effective detection and isolation of atomic columns from surrounding background areas. The model was trained on open-source STEM simulation datasets with a variety of structures [ <a class="xref-link" href="#r2">2</a>] and fine-tuned on real data, allowing it to gain versatility while achieving high accuracy. Post-processing with this saliency model prepares images for blob detection, making it suitable even in low-dose imaging cases. <div class="fig panel" id="fg001"> <a class="named-anchor" id="fg001"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/1d61f87d-34b0-47f0-b8e0-93f35f9f8506/ScienceOpen/image/127_6725ae086001c-IngyuYooAPMC2025_fig1.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 1.</div> <div class="caption" id="d16365e126"> <p class="first" dir="auto" id="d16365e127">Overview of the coordinate extraction process applied to a WSe2 monolayer: Pyramid Saliency Model for background separation, RegionProps clustering for center coordinates extraction, and Conversion of coordinate stack to Graph dataset for defect analysis. </div> </div> </div> Cluster analysis ; Computer science ; Artificial intelligence Machine Learning in Materials Science