Index

We will build an end-to-end RFIC design flow (schematic → layout → EM → co-simulation) both with open-source tools and in commercial environments, and correlate the results.The work includes IC design (schematic, layout, verification) in Cadence/ADS and Ansys HFSS/CST, as well as with open-source tools (Qucs-S/Ngspice/Xyce, KLayout/gdsfactory, openEMS, scikit-rf).

We will build an end-to-end RFIC design flow (schematic → layout → EM → co-simulation) both with open-source tools and in commercial environments, and correlate the results.The work includes IC design (schematic, layout, verification) in Cadence/ADS and Ansys HFSS/CST, as well as with open-source tools (Qucs-S/Ngspice/Xyce, KLayout/gdsfactory, openEMS, scikit-rf).

We will build an end-to-end RFIC design flow (schematic → layout → EM → co-simulation) both with open-source tools and in commercial environments, and correlate the results.The work includes IC design (schematic, layout, verification) in Cadence/ADS and Ansys HFSS/CST, as well as with open-source tools (Qucs-S/Ngspice/Xyce, KLayout/gdsfactory, openEMS, scikit-rf).

The goal is to develop an algorithm or machine-learning approach that automates parts of RFIC design using open-source design tools. We focus on passive components such as transformers, baluns, and antennas. Based on desired performance parameters (e.g., target frequency, bandwidth, insertion loss, impedances, efficiency), a tool should generate the optimal geometry.

The goal is to develop an algorithm or machine-learning approach that automates parts of RFIC design using open-source design tools. We focus on passive components such as transformers, baluns, and antennas. Based on desired performance parameters (e.g., target frequency, bandwidth, insertion loss, impedances, efficiency), a tool should generate the optimal geometry.

The goal is to develop an algorithm or machine-learning approach that automates parts of RFIC design using open-source design tools. We focus on passive components such as transformers, baluns, and antennas. Based on desired performance parameters (e.g., target frequency, bandwidth, insertion loss, impedances, efficiency), a tool should generate the optimal geometry.

Description As part of a campus Joint Communication and Sensing (JCAS) system, machine learning methods for person detection and classification based on radar data are to be developed. Students will research and select suitable datasets (e.g., point clouds or range–Doppler data) and prepare them...

The high-frequency power amplifier (RF PA) plays a central role in every transmission and reception chain. Such amplifiers are not only used in classic applications such as mobile communications, aerospace and defence, but also in medical technology – especially in magnetic resonance imaging (MRI). In MRI systems, a powerful RF pulse is emitted at the so-called Larmor frequency – i.e. the frequency at which the hydrogen nuclei (protons) precess in the static magnetic field. In a 3-Tesla MRI system, this frequency is approximately 128 MHz. The RF pulse excites the spins of the protons. When they return to their ground state, the protons emit energy in the form of RF signals, which are measured and used for image reconstruction.

The high-frequency power amplifier (RF PA) plays a central role in every transmission and reception chain. Such amplifiers are not only used in classic applications such as mobile communications, aerospace and defence, but also in medical technology – especially in magnetic resonance imaging (MRI). In MRI systems, a powerful RF pulse is emitted at the so-called Larmor frequency – i.e. the frequency at which the hydrogen nuclei (protons) precess in the static magnetic field. In a 3-Tesla MRI system, this frequency is approximately 128 MHz. The RF pulse excites the spins of the protons. When they return to their ground state, the protons emit energy in the form of RF signals, which are measured and used for image reconstruction.