Multiphysics-Informed ML-Assisted Chiplet Floorplanning for Heterogeneous Integration

The floorplan of chiplets in heterogeneously integrated systems-in-package (SiPs) must consider multiphysics (electrical, thermal, and mechanical) performance and meet positional constraints during optimization. This article sets forth an efficient framework for chiplet floorplanning subject to positional and multiphysics-performance-based constraints. Traditional multiphysics simulations, often impractical in optimization due to high computational cost, are replaced by a high-fidelity and efficient conditional image generative model via image-based machine learning (ML). This model is accurate and capable of performing real-time prediction of multiphysics performance throughout 3-D SiPs. Utilizing the image-based ML model for fast performance assessment, we further accelerate the physical design by developing a novel and highly parallelizable dynamic rank-revealing (RR) algorithm for solving the underlying constrained optimization problem. We leverage this algorithm to optimize the position of the chiplets subject to multiphysics performance directly without floorplan representation or convexification techniques while meeting a multitude of constraints. The same ML model and constraints are also integrated into a state-of-the-art corner block list (CBL) floorplan representation under a simulated annealing (SA) optimization framework. The accuracy and efficiency of the proposed optimization method are demonstrated in the floorplanning of chiplets on an interposer subject to thermal constraints, and by comparisons against ML-assisted SA-CBL for performing the same task.