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    Evaluation of Interconnects up to 100 GHz Using Machine Learning
    This thesis presents a contribution to the evaluation and modeling of high-speed electrical interconnects on printed circuit boards up to 100 GHz using machine learning. Parametric studies of different models are conducted with efficient numerical methods. The crosstalk is analyzed for different interconnect types. Machine learning methods are introduced as an alternative design method. They are evaluated regarding the reliability of the predictions and the necessary effor
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    Interconnects at 50-100 Gbps
    The aim of the project is the study of digital links with data rates of 50 Gbps and higher. Data rates of digital links are increasing constantly. This gives rise to effects that complicate error free signal propagation and sometimes render it impossible. Crosstalk occurs due to small distances between transmission lines. Furthermore, discontinuities lead to reflections, which interfere with the signal. The goal is the characterization and the design of interconnects that allow for high data rates. By using different line codings signals with lower bandwidths are generated which makes them less sensitive to high-frequency disturbances. For the evaluation of the signal integrity tools that were developed at the institute and allow for a fast generation of eye diagrams are used as well as several commercial tools.
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