@article{BuchnerHausladenBartletal., author = {Buchner, Philipp and Hausladen, Matthias and Bartl, Mathias and Bachmann, Michael and Schreiner, Rupert}, title = {High current field emission from Si nanowires on pillar structures}, series = {Journal of Vacuum Science and Technology B: Nanotechnology and Microelectronics}, volume = {42}, journal = {Journal of Vacuum Science and Technology B: Nanotechnology and Microelectronics}, number = {2}, publisher = {AIP}, issn = {2166-2754}, doi = {10.1116/6.0003384}, abstract = {We investigate the influence of the geometry and doping level on the performance of n-type silicon nanowire field emitters on silicon pillar structures. Therefore, multiple cathodes with 50 by 50 pillar arrays (diameter: 5 μm, height: 30 μm, spacing: 50 μm) were fabricated and measured in diode configuration. In the first experiment, we compared two geometry types using the same material. Geometry 1 is black silicon, which is a highly dense surface covering a forest of tightly spaced silicon needles resulting from self-masking during a plasma etching process of single crystal silicon. Geometry 2 are silicon nanowires, which are individual spaced-out nanowires in a crownlike shape resulting from a plasma etching process of single crystal silicon. In the second experiment, we compared two different silicon doping levels [n-type (P), 1-10 and <0.005 Ω cm] for the same geometry. The best performance was achieved with lower doped silicon nanowire samples, emitting 2 mA at an extraction voltage of 1 kV. The geometry/material combination with the best performance was used to assemble an integrated electron source. These electron sources were measured in a triode configuration and reached onset voltages of about 125 V and emission currents of 2.5 mA at extraction voltages of 400 V, while achieving electron transmission rates as high as 85.0\%.}, language = {en} } @article{TanSchreinerHausladenetal., author = {Tan, Jing Jie and Schreiner, Rupert and Hausladen, Matthias and Asgharzade, Ali and Edler, Simon and Bartsch, Julian and Bachmann, Michael and Schels, Andreas and Kwan, Ban-Hoe and Ng, Danny Wee-Kiat and Hum, Yan-Chai}, title = {SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis}, series = {Journal of Vacuum Science \& Technology B}, volume = {43}, journal = {Journal of Vacuum Science \& Technology B}, number = {6}, publisher = {AVS}, doi = {10.1116/6.0005068}, 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 convolutional neural network architecture incorporating attention mechanisms was trained for multiclass 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.}, language = {en} }