Thermal, mechanical, and electrical properties of Si-stacked nanosheet transistors using machine learning interatomic potentials

Thermal and mechanical properties play a key role in optimizing the performance of nanoelectronic devices. In this study, the lattice thermal conductivity (κL) and elastic constants of Si nanosheets at different sheet thicknesses were determined using recently developed machine learning interatomic potentials (MLIPs). A Si nanosheet with a minimum thickness of 10 atomic layers was used for model training to predict the properties of sheets with greater thicknesses. The training dataset was efficiently constructed using stochastic sampling of the potential energy surface (PES). Density functional theory (DFT) calculations were used to extract the MLIP, which served as the basis for further analysis. The Moment Tensor Potential (MTP) method was used to obtain the MLIP in this study. The results showed that, at sub-6 nm sheet thickness, the thermal conductivity dropped to ∼ 7 % of its bulk value, whereas some stiffness tensor components dropped to ∼ 3 % of the bulk values. These findings contribute to the understanding of heat transport and mechanical behavior of ultrathin Si nanosheets, which is crucial for designing and optimizing nanoelectronic devices. The technological implications of the extracted parameters on nanosheet field-effect transistor (NS-FET) performance at advanced technology nodes were evaluated using TCAD device simulations.