Application of multiscale approach and data sciences for modeling thermal conductivity in layered structures

Modeling thermal properties of layered structures is currently a popular area of scientific research. This is due to the constantly growing speed of operation of microelectronic elements often based on layered structures that release more and more energy during operation in the form of heat which must be removed to avoid overheating and loss of functional properties of devices. The paper presents an integration approach that allows one to combine the methods of multiscale modeling and data analysis. It is shown that application of this approach makes it possible to obtain a new quality when solving the problem of constructing a model of heat transfer in a two-layer GaAs/AlAs structure. The effectiveness of use of machine learning methods for analyzing the dependence of the effective thermal conductivity coefficient of laminated materials on structural features and external factors is shown. The development of the proposed approach will be able to provide formation of information for reasonable selection of materials for layered structures for microelectronic devices.