[
    {
        "id": "osp-15844",
        "type": "article-journal",
        "title": "A Physics-Guided Transformer Framework for Electromigration Analysis in Multi-Segment Interconnects",
        "author": [
            {
                "family": "Stoikos",
                "given": "Pavlos"
            },
            {
                "family": "Pathania",
                "given": "Anuj"
            },
            {
                "family": "Floros",
                "given": "George"
            }
        ],
        "URL": "https://omanscience.com/en/articles/a-physics-guided-transformer-framework-for-electromigration-analysis-in-multi-segment-interconnects",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]