Modern device engineering is driven by computational sciences, numerical methods, and simulation techniques. Especially in the wide field of nanotechnology, simulated experiments and virtual prototyping aid the design process of novel electronic devices. Due to expensive fabrication processes and the vast amount of design parameters, numerical simulations are the only viable way to optimize the devices. Nanotechnology also opens the possibility to design new materials and directly tailor their properties for optimal device performance. Here numerical models help to screen the mere infinite number of possible materials for promising candidates. Because of the difficult probing and characterization of nanostructured materials, numerical models help to investigate and understand the underlying physical effects quantitatively.
To cover all aspects of electronic devices, numerical models and simulations of multiple length and time scales are necessary. On the microscopic level, first-principle calculations such as Density Functional Theory (DFT) are used to determine the electronic-structure of molecules and materials. On the mesoscopic level, statistical models such as kinetic Monte Carlo (kMC) simulations are used to compute charge carrier mobilities or charge carrier densities in materials. On the macroscopic level, continuum models such as Drift-Diffusion (DD) simulations are used to compute IV-characteristics, effective current, or heat transport. Multiscale approaches aim to link models of different length scales and provide consistent simulation models with high accuracy.
During the last decade, machine learning entered almost every field of research. In materials science machine learning approaches such as Neural Networks in many flavors (CNN, RNN, GNN, …) are used for material screening tasks. In the field of multiscale device simulation machine learned models are used to minimize the amount of computationally intensive first-principles calculations and provide after sufficient training a cheap model to predict desired electronic properties such as bandgaps, energy levels, or charge transfer parameters.
Currently our research is focused on the following Applications:
Within this context we make use of the following Methods:
For further information on our research, browse through our projects and our recent publications.
TUM Innovation Networks
Artificial Intelligence Powered Multifunctional Materials Design (ARTEMIS)
DFG Project II: DFG Initiative of Excellence e-Conversion Cluster
DFG Project I: DFG Initiative of Excellence e-Conversion Cluster
- IGSSE Project: TEAM 11.02 CONTROL
If you are interested in doing a BSc / MSc thesis or internship, take a look at our current Thesis Offers and don’t hesitate to contact us to ask for a thesis topic in your field of interest.