Developement of reliable neural network potential for metal–semiconductor interface reaction: case study for Ni silicidation

Molecular dynamics using classical interatomic potentials can provide valuable information at the atomistic scale. However, when the simulation involves chemical reactions of bond breaking and forming along with mixed bonding characters, it is challenging to develop an accurate force field for the system and sometimes practically impossible. In this respect, the machine-learning potentials are highly anticipated since they are based on flexible mathematical structures with no pre-fixed form. In this presentation, we discuss the process of constructing a reliable neural network potential (NNP) for a challenging metal–semiconductor interface reaction, with example of thermally activated Ni silicidation. We present a systematic way to build up the training set that can describe the interface reaction. We also introduce some of the techniques we utilized for higher reliability and efficiency, including Gaussian density function weighting, principle component analysis training etc. In order to obtain the prediction uncertainty for certain local configurations, we adopt replica NNPs that are trained directly on the atomic energy of the reference NNP. Finally, we suggest the underlying mechanism of abnormal crystal phase growth from ultra-thin Ni film silicidation.