ML-EMFI: A Machine Learning-Driven Pre-Silicon Electromagnetic Fault Injection Security Evaluation for Robust IC Design

Electromagnetic Fault Injection (EMFI) is a critical threat to the security and reliability of integrated circuits (ICs), capable of causing data corruption, system failures, and information leakage. To address these challenges, we propose a novel machine learning-driven framework for efficient EMFI analysis in the pre-silicon design stage. Our approach integrates electromagnetic simulation, machine learning guided modeling, and layout level voltage-current analysis to accurately predict induced currents and evaluate their impact on IC performance and security. A core innovation of this work is the machine learningguided identification of “cutout” regions-areas most impacted by EM fault injection-significantly reducing simulation runtime while maintaining high accuracy. The framework trains a model on a diverse dataset of metal layer configurations and coil distances and thus, allows precise targeting of critical areas for simulation. Finally, by analyzing dynamic current distribution across critical metal layers, the method effectively assesses design robustness against EMFI-induced timing violations and faults. Experimental results demonstrate that this framework achieves a substantial reduction in simulation run-time compared to traditional full-geometry approaches. Furthermore, the simulation results are validated by experimental data, confirming the framework's accuracy and reliability. Thus, this framework provides a practical, fast, and scalable solution to enhance IC security and reliability against EMFI.