Using decision trees for fast error identification in physics-based modeling of PCB structures

Physics-based (PB) via models for printed circuit board (PCB) simulations are computationally efficient and have proven accurate up to 40 GHz. However, they require validation against time-intensive full-wave (FW) electromagnetic solvers when applied to faster links at 100 GHz. This study presents an error identification approach using a decision tree (DT) that categorizes designs into low- and high-error classes based on PCB design parameters, enabling targeted FW validation only where needed. The DT's interpretability reveals which geometric and material parameters most influence PB model accuracy.

Content

Subjects

Decision trees

machine learning

model validation

printed circuit boards (PCBs)

signal integrity (SI)

DDC Class

537: Electricity and Electronics

600: Technology