SiMiC: Context-Aware Silicon Microstructure Characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis

• URL: http://arxiv.org/abs/2601.17048v1
• Date: Wed, 21 Jan 2026 04:36:42 GMT
• Title: SiMiC: Context-Aware Silicon Microstructure Characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis
• Authors: Jing Jie Tan, Rupert Schreiner, Matthias Hausladen, Ali Asgharzade, Simon Edler, Julian Bartsch, Michael Bachmann, Andreas Schels, Ban-Hoe Kwan, Danny Wee-Kiat Ng, Yan-Chai Hum,
• Abstract summary: We propose SiMiC: Context-Aware Silicon Microstructure characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis.<n>By leveraging deep learning, our approach efficiently extracts morphological features-such as size, shape, and apex curvature-from SEM images.<n>A specialized dataset of silicon-based field-emitter tips was developed, and a customized CNN architecture incorporating attention mechanisms was trained for multi-class microstructure classification and dimensional prediction.
• Score: 1.4613896920385958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate characterization of silicon microstructures is essential for advancing microscale fabrication, quality control, and device performance. Traditional analysis using Scanning Electron Microscopy (SEM) often requires labor-intensive, manual evaluation of feature geometry, limiting throughput and reproducibility. In this study, we propose SiMiC: Context-Aware Silicon Microstructure Characterization Using Attention-Based Convolutional Neural Networks for Field-Emission Tip Analysis. By leveraging deep learning, our approach efficiently extracts morphological features-such as size, shape, and apex curvature-from SEM images, significantly reducing human intervention while improving measurement consistency. A specialized dataset of silicon-based field-emitter tips was developed, and a customized CNN architecture incorporating attention mechanisms was trained for multi-class microstructure classification and dimensional prediction. Comparative analysis with classical image processing techniques demonstrates that SiMiC achieves high accuracy while maintaining interpretability. The proposed framework establishes a foundation for data-driven microstructure analysis directly linked to field-emission performance, opening avenues for correlating emitter geometry with emission behavior and guiding the design of optimized cold-cathode and SEM electron sources. The related dataset and algorithm repository that could serve as a baseline in this area can be found at https://research.jingjietan.com/?q=SIMIC

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