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Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive, especially for 3D structures where runtime escalates steeply with mesh complexity. This sharply limits multi-objective design space exploration. Existing machine-learning surrogates map a fixed set of design parameters to a few scalar device metrics, discarding the underlying physics and losing transferability across device geometries and families. A physics-informed graph attention network (GAT) surrogate is proposed. It operates directly on t

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