EMSpice 3: Full-chip Temperature-Aware Multiphysics Electromigration and IR-Drop Analysis

2026-04-12

AI Summary

This work addresses the inaccuracy in electromigration (EM) and thermomigration (TM) prediction caused by conventional power grid reliability assessment methods that rely on average temperature assumptions while neglecting spatially non-uniform thermal distributions. To overcome this limitation, we propose the first full-chip EM/TM sign-off framework incorporating realistic chip-level thermal maps. The methodology establishes a multiphysics analysis flow enabling temperature-aware transient simulation, iterative resistance updates, and Monte Carlo lifetime analysis, accelerated via an extended rational Krylov model order reduction technique and seamlessly integrated into Synopsys ICC and Fusion Compiler. Experimental results demonstrate that, under identical average temperatures, time-to-failure varies by up to 70% and 50% for RISC-V and ARM cores, respectively; simulation speedups of 1.18–1.50× are achieved; and Monte Carlo analysis reveals markedly different sensitivities to process variations across architectures, thereby validating the necessity and efficacy of the proposed workload- and thermal-map-aware evaluation paradigm.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

Abstract

In this work, we present EMSpice 3, a full-chip temperature-aware multiphysics framework for coupled electromigration (EM), thermomigration (TM), and IR-drop analysis of power-grid networks. It unifies extracted netlists, configurable parameters, and optional chip-level thermal maps into a single flow supporting temperature-aware immortality screening, transient EM/TM stress simulation with iterative resistance updates, and optional Monte Carlo lifetime analysis. To accelerate large-tree simulations, EMSpice 3 integrates an extended rational Krylov reduction method into the transient solver without loss of accuracy. It also interfaces with Synopsys ICC and Fusion Compiler for practical deployment. By incorporating realistic spatial thermal maps into the reliability loop, the framework enables map-aware EM sign-off beyond average-temperature assumptions. Experiments on six designs show that spatial thermal variation significantly impacts EM reliability even with identical average temperature. For a RISC-V core, equal-average thermal profiles yield over 70% spread in time to failure (TTF), while an ARM Cortex-A core shows nearly 50%. The Krylov-accelerated solver achieves 1.18x - 1.50x runtime reduction. Monte Carlo analysis reveals strong design dependence: under 20% variation in diffusivity and critical stress, TTF variation is about 25\% for RISC-V but only 0.03% for ARM. These results demonstrate that EMSpice 3 enables practical, map-aware, and workload-aware full-chip EM reliability assessment.

Problem

• electromigration
• temperature-aware
• IR-drop
• multiphysics
• reliability

Innovation

• electromigration
• temperature-aware analysis
• multiphysics simulation
• Krylov reduction
• IR-drop