Kvantmekanisk Beskrivning av Fullständiga Halvledaranordning

Forskningsprojekt, 2022 –

The project concerns first-principles modeling of materials and interfaces that constitute semiconductor micro- and nanodevices. The work will build on a modeling methodology that I have developed, that considers materials in a realistic way, including imperfections, intrinsic effects, and temperature. The main objective is to decrease the computational cost of said approach, yielding it feasible to apply it in the studies of a device comprising multiple functional layers and interfaces, within the timeframe of a single project. To achieve this objective, I plan to train an Artificial Neural Network to replace the most computationally demanding step of my methodology - Ab-initio Molecular Dynamics simulations of temperature effects. I will use an approach that has been shown to work on simpler problems - the force-field method, but, capitalising on my expertise, apply it to a much more demanding systems containing defects and charge-lattice interactions (polarons). Once the ANN model is implemented in my methodology, it will allow for gaining insights into crucial local phenomena that partially govern the functioning of microdevices, within a fraction of time that is now necessary to carry out these calculations. I plan to apply this newly developed method to perovskite solar cells and tunnel field effect transistors, in collaboration with experimentalists. If successful, this methodology will constitute a great aid in microdevice optimisation.