@article{62643,
  author       = {{Schwabe, Tobias and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and Scheytt, J. Christoph}},
  journal      = {{Journal of Lightwave Technology}},
  keywords     = {{Integrated circuit modeling, Capacitance, Silicon, Modulation, Adaptation models, Semiconductor device modeling, Bandwidth, Data communication, electrooptical transmitter, equalization, free-carrier-plasma dispersion effect, modelling, optical modulator, phase shifter, silicon photonics}},
  number       = {{1}},
  pages        = {{255--270}},
  title        = {{{Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology}}},
  doi          = {{10.1109/JLT.2024.3450949}},
  volume       = {{43}},
  year         = {{2025}},
}

@inproceedings{62641,
  author       = {{Kruse, Stephan and Diri, Jabil and Mager, Thomas and Kress, Christian and Scheytt, J. Christoph}},
  booktitle    = {{2025 55th European Microwave Conference (EuMC)}},
  keywords     = {{Optical fibers, Integrated optics, Semiconductor device measurement, Laser radar, Optical device fabrication, Photonic integrated circuits, Microwave theory and techniques, Optical fiber devices, Plastics, Substrates, Microwave photonics, photonic radar, optical LO distribution, mechatronic integrated device (MID)}},
  pages        = {{127--130}},
  title        = {{{Electrooptical Integration of an Electronic Photonic Integrated Circuit Into Plastic Substrates Using Mid-Technology}}},
  doi          = {{10.23919/EuMC65286.2025.11235121}},
  year         = {{2025}},
}

@article{55999,
  abstract     = {{Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor´s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g-1 to H2 concentrations (0-800 ppm) and relative humidity (0-70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations.}},
  author       = {{Baier, Dominik  and Krüger, Alexander  and Wagner, Thorsten  and Tiemann, Michael and Weinberger, Christian}},
  issn         = {{2227-9040}},
  journal      = {{Chemosensors}},
  keywords     = {{resistive gas sensor, chemiresistor, semiconductor, metal oxide, In2O3, mesoporous, hydrogen, humidtiy, machine learning, sustainable}},
  number       = {{9}},
  pages        = {{178}},
  publisher    = {{MDPI}},
  title        = {{{Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O}}},
  doi          = {{10.3390/chemosensors12090178}},
  volume       = {{12}},
  year         = {{2024}},
}