Enhanced Electromagnetic Interference Sensitivity Prediction via Multi-Modal Graph Neural Network and HyperScore Validation

**Abstract:** This paper proposes a novel system, Multi-Modal Graph Enhanced Electromagnetic Interference Sensitivity Predictor (MM-GEISP), for accurately predicting electromagnetic interference (EMI) sensitivity in printed circuit boards (PCBs). Leveraging a combination of geometric data, material properties, and circuit topology, integrated into a graph neural network architecture, MM-GEISP offers a significant improvement over traditional Finite Element Method (FEM) simulations. The system's output is rigorously validated using a HyperScore framework which introduces a probabilistic weighting scheme for increased accuracy and reliability, enabling faster and more cost-effective design iterations. It offers a 10x speedup in sensitivity mapping relative to gold-standard FEM simulations, allowing rapid identification of problematic areas within a PCB layout and facilitates proactive mitigation strategies, aimed at improving overall signal integrity.