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Institute of Solid State Physics
Autonomous Driving for Nanocars
Machine Learning, Exp.-Theory Collaboration, STM, Surface Science
Advanced process nodes for analog radiation tolerant ICs
Advanced CMOS process nodes like 28 nm and 40 nm have been introduced in commercial products several years ago. Usage of these processes for high performance digital circuits is being continuously and broadly exploited. But their applicability for analog functions of mixed-signal systems is still to be further explored. In particular this is an interesting research are for particle physics experiments, where scaled down MOS transistor features could offer advantages for radiation tolerance, which is a subject of this project.
Contact: Alicja Michalowska-Forsyth (firstname.lastname@example.org)
Modeling and Characterization of Semiconductor Devices at Cryogenic Temperatures
The objective of the master thesis is to investigate the behavior of semiconductor devices (resistors, MOS-transistors) in a CMOS-technology at temperatures below 20K. For this, both, simulations on a device-physics level and measurements of test devices should be performed. The simulations will be done in Synopsis TCAD using the latest models. Their results should clearly show the influence of temperature on electrical device properties like resistance, gain and bandwidth. Since semiconductor performance is usually not modeled in these regimes, advanced physical models can be implemented using a C++ API to account for effects not yet implemented in the software.
The advent of Machine Learning methods has unlocked great potential in computational studies. In particular the exploration of surface structures, that was previously thought to be completely unfeasible, has surged in the recent years. At the same time, machine learning studies are often criticized for their lack of physical insight.
Goal: Development of atomistically motivated structure-to-property relationships for heat transport in organic semiconductors – a property, that is crucial for device operation, but is still largely unexplored such that the suggested studies can have a huge impact.
The molecule HATCN is a strong electron acceptor that is commercially used in OLEDs to modify the property of metal substrates. Adsorbed on silver, this molecule shows unusual, fascinating physics. At low coverage, the molecule forms honeycomb patterns, which can be exploited as epitaxial growth template. When the coverage is increased, however, the first monolayer rearranges. This drastically changes the material properties, in particular the system’s work function.