CODE AND MESH AGNOSTIC NON-LINEAR MULTISCALE ANALYSIS AND MACHINE LEARNING MODELS FOR DESIGN AND ANALYSIS OF HETEROGENEOUSLY INTEGRATED ELECTRONIC PACKAGES

Modeling and simulation play a pivotal role in engineering and research, enabling cost effective solutions for design, manufacturing, and failure analysis, especially where physical testing is infeasible. This work explores numerical methods for multi-scale domains, where structures span diverse length scales, presenting unique challenges in meshing and accuracy. Advanced approaches such as domain decomposition and global-local methods are discussed, with an emphasis on their application in heterogeneous integration (HI) for advanced packaging. HI, which addresses the limitations of Moore’s Law, integrates diverse components into 2.5D and 3D architectures but introduces complex mechanical and thermo-mechanical challenges. This research addresses gaps in multi-scale numerical frameworks, proposing novel methods to handle non-linear physical evolution while maintaining compatibility with existing tools. A non-intrusive global-local inspired methodology that couples the local subdomain back to the global subdomain was implemented to increase the accuracy in non-linear multi-scale simulations involving evolution at local scale. The developed framework was then generalized to solve rate dependent and rate independent phenomenon. The work further extends into numerical methods for design of HI packages as well. Unlike detailed analysis, the design stage analysis prioritizes speed of computation with a first order accuracy of results. This is achieved using machine learning techniques for efficient design space exploration in HI. The study overall aims to advance computational frameworks tailored for accuracy in reliability analysis and speed in design stages, focusing on semiconductors and advanced packaging applications.