I have broad interests in various fields of computational science. A rough sketch is as follows:

Semiconductor Device Simulation

I'm working on the fundamental building blocks of each digital circuits, namely transistors. In contrast to full circuit-level simulations, which deal with the interaction of many transistors, my focus is on a better understanding and a more predictive simulation of a single transistor. The drift-diffusion model has been the method of choice for several decades, yet it fails to describe modern, scaled-down devices. My approach is to solve the semi-classical Boltzmann equation using the deterministic spherical harmonics expansion method instead, which provides the accuracy of Monte Carlo methods at a fraction of the computational expense. Read more...

Numerical Solution of PDEs

The model equations I'm dealing with in semiconductor device simulation are partial differential equations (PDEs). Since analytic solutions can only be obtained in rather simple settings, various families of numerical solution schemes such as finite element methods exist. My interest is in the efficient implementation and composition of these schemes in one-, two-, and three- spatial dimensions. Read more...

GPU & Accelerator Computing

While central processing units (CPUs) doubled their speed approximately every two years up until the early 2000s, physical limitations no longer allow such improvements for a single core. Instead, multiple cores can now be found on modern CPUs and hence it is required to employ suitable parallel algorithms in order to make use of the higher performance now provided via additional cores rather than higher clock frequency. For certain parallel algorithms it may even pay off to run general purpose computations on graphics processing units (GPUs), which are by nature tailored to efficiently work in parallel. Read more...