XGBoost trained on synthetic data to extract material parameters of organic semiconductors

E Knapp, M Battaglia, T Stadelmann, S Jenatsch, B Ruhstaller

8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021

https://160.85.104.64/handle/11475/22414

In this paper, the authors combine the use of machine learning and a semiconductor device modelling tool (Setfos) to extract the material parameters from measurements and inturn train their machine learning model with synthetic training data originating from a semiconductor simulator. In a second step, the machine learning model is applied to a measured data set and determines the underlying material parameters. This novel and reliable method for the determination of material parameters paves the way to further device performance optimization.