Vision-Based Transformer Model for Fast Simulation of Advanced Packaging Structure

Accurate electromagnetic (EM) modeling of electronic package structures is critical for signal and power integrity analysis, yet full-wave simulations on fine meshes are often computationally expensive. Deep learning and neural network models have recently attracted considerable attention as alternative solutions. However, with the continuous expansion of the design space in advanced packaging, conventional surrogate architectures such as FCNNs, CNNs, and RNNs start to exhibit limitations in scalability. Therefore, in this paper, we propose two vision-based transformer models that eliminate the need for design parameters and leverage self-attention mechanism for efficient package-level EM and multi-physics simulation. The first framework employs a Vision Transformer (ViT) to directly predict frequency-domain responses (S-parameters) from structural images of the package. By capturing global spatial dependencies through self-attention, the ViT-based model achieves high accuracy while significantly reducing computational cost compared to conventional EM solvers. The second framework introduces a transformer-based super-resolution neural network designed for multiphysics co-simulation scenarios. Specifically, a physical structure is simulated with a coarse mesh using an EM solver to provide power input to the thermal solver, which subsequently generates low-resolution temperature profile images. The super-resolution neural network then refines these results to produce high-resolution temperature distributions, akin to those obtained through numerical simulations with a fine mesh. Various numerical examples are simulated to validate the capability and improvement of the proposed method.