Neural Network Surrogate Model for Junction Temperature and Hotspot Position in 3D Multi-Layer High Bandwidth Memory (HBM) Chiplets Under Varying Thermal Conditions

As the demand for computational power increases, high-bandwidth memory (HBM) has become a critical technology for next-generation computing systems. However, the widespread adoption of HBM presents significant thermal management challenges, particularly in multilayer through-silicon-via (TSV) stacked structures under varying thermal conditions, where accurate prediction of junction temperature and hotspot position is essential during the early design. This work develops a datadriven neural network model for the fast prediction of junction temperature and hotspot position in 3D HBM chiplets. We trained the model with a data set of 13,494 different combinations of thermal condition parameters, sampled from a vast parameter space characterized by high-dimensional combination (up to 3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">27</sup>), using Latin hypercube sampling (LHS) for parameter space sampling, can accurately infer the junction temperature and hotspot position for any thermal conditions in the parameter space. Moreover, it shows good generalizability for other thermal conditions not considered in the parameter space. The data set is constructed using accurate finite element solvers. This method not only minimizes the reliance on costly experimental tests and extensive computational resources for finite element analysis but also accelerates the design and optimization of complex HBM systems, making it a valuable tool for improving thermal management and performance in high-performance computing applications.