TY - CHAP A1 - Asgharzade, Ali A1 - Bartl, Mathias A1 - Hausladen, Matthias A1 - Chen, Yang A1 - Buchner, Philipp A1 - Edler, Simon A1 - Bachmann, Michael A1 - She, Juncong A1 - Schreiner, Rupert T1 - Investigation on the influence of internal voltage drops on the emission behavior of silicon field emission arrays using CMOS image sensor T2 - 38th International Vacuum Nanoelectronics Conference (IVNC) N2 - In this paper we report on an improved measurement and analysis method for determining the emission current distribution of field emitter arrays with CMOS sensors. It can also be used for field emission arrays where not all surfaces of the emitters are at the same electrical potential (e.g. for p-doped Si tips, where a strong current saturation occurs). To demonstrate the functionality of this method, a n-doped field emission array was measured using a variable external series resistance. Brightness variations in emission spots were analyzed to re-calculate the known series resistances for comparison. The re-calculated value is in good agreement with the actual value of the resistance. This method offers a quantitative approach to assess internal voltage drop effects on field emission using optical readout KW - field emission imaging KW - field emission KW - CMOS sensor Y1 - 2025 U6 - https://doi.org/10.1109/IVNC65669.2025.11120937 PB - IEEE ER - TY - JOUR A1 - Buchner, Philipp A1 - Hausladen, Matthias A1 - Bartl, Mathias A1 - Bachmann, Michael A1 - Schreiner, Rupert T1 - High current field emission from Si nanowires on pillar structures JF - Journal of Vacuum Science and Technology B: Nanotechnology and Microelectronics N2 - 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%. KW - Doping KW - Electron sources KW - Nanowires KW - Plasma processing KW - Field emitter arrays KW - Triodes KW - Semiconductors Y1 - 2024 U6 - https://doi.org/10.1116/6.0003384 SN - 2166-2754 VL - 42 IS - 2 PB - AIP ER - TY - CHAP A1 - Barik, Ranjan Kumar A1 - Rawat, Vikram Singh A1 - Manna, Subhrajit A1 - Bandyopadhyay, Ayan Kumar A1 - Hausladen, Matthias A1 - Asgharzade, Ali A1 - Schreiner, Rupert T1 - On Chip Electron Gun Design Using Silicon Tip Field Emitter Array T2 - 2025 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 14-18. Dezember 2025, Kochi, India N2 - Silicon based silicon tip array emitter has developed at OTH Regensburg, Germany. This array emitter is the basis for development of a novel electron gun. This field emitter electron sources were fabricated using laser-micromachining technique followed by MEMS technology. In this work, the design and development of electron gun using complete silicon structure is reported. The goal of this work is to develop electron gun using less effort, easy and hassle free technique. Y1 - 2025 SN - 979-8-3315-3722-7 U6 - https://doi.org/10.1109/MAPCON65020.2025.11426578 PB - IEEE ER - TY - JOUR A1 - Tan, Jing Jie A1 - Schreiner, Rupert A1 - Hausladen, Matthias A1 - Asgharzade, Ali A1 - Edler, Simon A1 - Bartsch, Julian A1 - Bachmann, Michael A1 - Schels, Andreas A1 - Kwan, Ban-Hoe A1 - Ng, Danny Wee-Kiat A1 - Hum, Yan-Chai T1 - SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis JF - Journal of Vacuum Science & Technology B N2 - 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. KW - Field emitter arrays KW - Quality assurance KW - Convolutional neural network KW - Deep learning KW - Image processing KW - Machine learning KW - Cold cathodes KW - Scanning electron microscopy KW - Electron sources KW - Chemical elements Y1 - 2025 U6 - https://doi.org/10.1116/6.0005068 VL - 43 IS - 6 PB - AVS ER -