Publications

The field of optical lithography is subject to intense research and has gained enormous improvement. However, the effort necessary for creating structures at the size of 20 nm and below is considerable using conventional technologies. This effort and the resulting financial requirements can only be tackled by few global companies and thus a paradigm change for the semiconductor industry is conceivable: custom design and solutions for specific applications will dominate future development (Fritze in: Panning EM, Liddle JA (eds) Novel patterning technologies. International society for optics and photonics. SPIE, Bellingham, 2021. https://doi.org/10.1117/12.2593229). For this reason, new aspects arise for future lithography, which is why enormous effort has been directed to the development of alternative fabrication technologies. Yet, the technologies emerging from this process, which are promising for coping with the current resolution and accuracy challenges, are only demonstrated as a proof-of-concept on a lab scale of several square micrometers. Such scale is not adequate for the requirements of modern lithography; therefore, there is the need for new and alternative cross-scale solutions to further advance the possibilities of unconventional nanotechnologies. Similar challenges arise because of the technical progress in various other fields, realizing new and unique functionalities based on nanoscale effects, e.g., in nanophotonics, quantum computing, energy harvesting, and life sciences. Experimental platforms for basic research in the field of scale-spanning nanomeasuring and nanofabrication are necessary for these tasks, which are available at the Technische Universität Ilmenau in the form of nanopositioning and nanomeasuring (NPM) machines. With this equipment, the limits of technical structurability are explored for high-performance tip-based and laser-based processes for enabling real 3D nanofabrication with the highest precision in an adequate working range of several thousand cubic millimeters.

The increasing complexity of connected and distributed mechatronic systems developed for safety-critical applications, as e.g. a powertrain of automated vehicles, makes their dependability evaluation a challenging task. Moreover, precise statements about the dependability metrics are of high interest for architectural decisions in the early stages of the design process. System dynamics, possible fault combinations as well as the sequence, duration and impact of various faults and the associated system states must be considered for a realistic evaluation and quantification of the failure behavior.In order to optimize the design of generic mechatronic systems at different abstraction levels and with different component characteristics, this paper examines a method to analytically quantify the stochastic behavior of a system. The proposed approach enables to significantly increase the computational efficiency of the safety analysis of generic fail-operational mechatronic systems without loss in accuracy by automating the dynamic evaluation of convolutional integrals. The application of the proposed safety analysis is demonstrated using an exemplary system with dynamic redundancy.

Modern industrial processes are large-scale, highly complex systems with many units and equipment. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significant cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both the variance structure and dynamic relationship. Compared with the original slow feature analysis (SFA) that can only model the one-step time dependence, long-term dependency slow feature analysis (LTSFA) proposed in this paper can understand the longer-term dynamics by an explicit expression of latent states of the process. An iterative algorithm is developed for the model parameter optimization and its convergency is proved. The model properties and theoretical comparison with existing dynamic models are presented. A process monitoring strategy is designed based on LTSFA. The results of two simulation case studies show that LTSFA has better system dynamics extraction capability, which reduces the violation rate of the residual for the 95% confidence interval from 40.4% to 3.2% compared to the original SFA, and can disentangle the quickly- and slowly-varying features. Several typical disturbances can be correctly identified by LTSFA. The monitoring results on the Tennessee Eastman process benchmark show the overall advantages of the proposed method both in the dynamic and nominal deviation detection and the monitoring accuracy

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures.

We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 [my]m^3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.