Subed Lamichhane | Searcharxiv

arXiv2025

EMSpice 2.1: A Coupled EM and IR Drop Analysis Tool with Joule Heating and Thermal Map Integration for VLSI Reliability

Electromigration (EM) remains a critical reliability concern in current and future copper-based VLSI circuits. As technology scales down, EM-induced IR drop becomes increasingly severe. While several EM-aware IR drop analysis tools have been proposed, few incorporate the real impact of temperature distribution on both EM and IR drop effects. In this work, we introduce EMSpice 2.1, an enhanced tool built upon the existing coupled IR-EM analysis framework, EMSpice 2.0, for EM-aware IR drop analysis. For the first time, EMSpice 2.1 uniquely integrates Joule heating effects and practical thermal maps derived from actual chip conditions. Additionally, it features improved interoperability with commercial EDA tools, facilitating more comprehensive EM and IR drop sign-off analysis. Our findings demonstrate that specific hotspot patterns significantly impact the lifetime of interconnects and overall chip reliability due to EM failures. Furthermore, our tool exhibits strong agreement with industry-standard tools such as COMSOL, achieving a speedup of over 200 times while maintaining high accuracy.

eess.SY↗

arXiv2025

BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase

In contrast to the assumptions of most existing Electromigration (EM) analysis tools, the evolution of EM-induced stress is inherently non-deterministic, influenced by factors such as input current fluctuations and manufacturing non-idealities. Traditional approaches for estimating stress variations typically involve computationally expensive and inefficient Monte Carlo simulations with industrial solvers, which quantify variations using mean and variance metrics. In this work, we introduce a novel machine learning-based framework, termed BPINN-EM- Post, for efficient stochastic analysis of EM-induced post-voiding aging processes. For the first time, our new approach integrates closed-form analytical solutions with a Bayesian Physics- Informed Neural Network (BPINN) framework to accelerate the analysis. The closed-form solutions enforce physical laws at the individual wire segment level, while the BPINN ensures that physics constraints at inter-segment junctions are satisfied and stochastic behaviors are accurately modeled. By reducing the number of variables in the loss functions through utilizing analytical solutions, our method significantly improves training efficiency without accuracy loss and naturally incorporates variational effects. Additionally, the analytical solutions effectively address the challenge of incorporating initial stress distributions in interconnect structures during post-void stress calculations. Numerical results demonstrate that BPINN-EM-Post achieves over 240x and more than 67x speedup compared to Monte Carlo simulations using the FEM-based COMSOL solver and FDM-based EMSpice, respectively, with marginal accuracy loss.

cs.LG↗