Résumé
Soft errors, typically caused by cosmic rays and particle strikes, have become a reliability challenge of modern electronics in aerospace, automotive, and high-performance computing. In advanced nodes with billions of transistors, a single strike can propagate errors, disrupting critical operations. As a result, traditional methods for estimating Soft Error Rate (SER), such as TCAD simulations and arithmetic formulas, are accurate but have become computationally expensive. In order to alleviate the computational burden, we propose a Model Order Reduction (MOR) approach to analyze charge collection dynamics induced by ionizing particles. More specifically, we start from the physics-based model of the diffusion-collection mechanism, then we discretize the fundamental equations in the 3D space, and formulate the system as a high-dimensional state-space problem. Finally, by applying the Extended Krylov Subspace (EKS) combined with Moment Matching (MM) techniques, we reduce computational overhead while maintaining accuracy. Experimental results demonstrate significant computational speedups while maintaining the accuracy of the full model.