Explainable Machine Learning Framework for RTL Timing Prediction: Bridging the Gap Between Black-Box AI and Hardware Design

This work addresses the trust deficit in AI-driven EDA through a Thermodynamic Intelligence Framework. By modeling netlists as dynamic graphs using GNNs and integrating SHAP/LIME for Explainable AI (XAI), we provide designer-actionable diagnostics. The model achieves 74.4% higher accuracy and demonstrates Zero-Shot transfer learning from 28nm training to 7nm prediction.