A Thermal Machine Learning Solver For Chip Simulation

Overlarge peak temperatures and stiff thermal gradients can fatally impact transistor performance, stress, aging, electromigration (EM), voltage drops and timing [18, 10]. Hence accurate prediction of the maximum temperature and thermal gradient on the chip becomes important for the performance and reliability of chip-packaging systems used in several applications such as 5G, automobiles and computational hardware for Artificial Intelligence. Conventional Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD) based thermal analysis is computationally expensive due to the enormous system parameter space in the form of stiff powermaps and wide range of Heat Transfer Coefficients (HTCs), die thicknesses and chip sizes. As a result, batches of simulations are required to be solved from scratch every time new system parameters of electronic chips are considered. Recently, a multitude of machine learning methods have been proposed to enhance and accelerate physics based numerical solvers in the context of electronic chip simulations.