aiqbench analysis
This research applies a physics-guided transformer architecture to accelerate electromigration stress prediction in semiconductor interconnects, addressing a critical reliability bottleneck as chips scale down. By integrating physical constraints directly into the model's training objective, the framework achieves significant speedups over traditional matrix exponential solvers while maintaining high accuracy on industry benchmarks. The work represents a meaningful convergence of advanced machine learning techniques with fundamental physics to solve complex hardware reliability problems.
A Physics-Guided Transformer Framework for Electromigration Analysis in Multi-Segment Interconnects
arXiv:2610.06464v1 Announce Type: cross Abstract: As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip interconnects. Accurate transient stress analysis is needed to identify wires susceptible to EM degradation, but applying physics-based solvers across many interconnects remains computationally expensive. This paper proposes a physics-guided transformer framework for fast EM stress prediction in multi-segment interconnect lines. The framework converts each line into geometry- and DC-aware segment tokens and uses transformer attention to capture line-level context. A lightweight query decoder then predicts stress at selected locations and time instants. The model is trained with an objective that combines normalized supervised regression, linewise relative-$L_2$ loss, and physics-guided continuity and terminal-flux terms. Experiments on IBM power grid benchmarks show that the proposed model achieves relative-$L_2$ error below 8\% and reaches up to 2459.68$\times$ speedup compared with the matrix exponential~solver.
Abstract:As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip interconnects. Accurate transient stress analysis is needed to identify wires susceptible to EM degradation, but applying physics-based solvers across many interconnects remains computationally expensive. This paper proposes a physics-guided transformer framework for fast EM stress prediction in multi-segment interconnect lines. The framework converts each line into geometry- and DC-aware segment tokens and uses transformer attention to capture line-level context. A lightweight query decoder then predicts stress at selected locations and time instants. The model is trained with an objective that combines normalized supervised regression, linewise relative-$L_2$ loss, and physics-guided continuity and terminal-flux terms. Experiments on IBM power grid benchmarks show that the proposed model achieves relative-$L_2$ error below 8\% and reaches up to 2459.68$\times$ speedup compared with the matrix exponential~solver.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.06464 [cs.LG]
(or arXiv:2610.06464v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.06464 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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