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

Summary

A physics-informed graph attention network surrogate runs directly on tetrahedral TCAD meshes and predicts electrostatic potential plus electron and hole quasi-Fermi levels at every mesh node. Combining a data loss with finite-volume current-continuity residuals embeds drift-diffusion physics into training, while graph-based operation lets models trained on few-fin FinFETs transfer to substantially larger multi-fin arrays. Deep-ensemble uncertainty powers active learning; benchmarks against Sentaurus Device show sub-volt field RMSE and orders-of-magnitude higher per-design throughput, with inference still under one second for large arrays.

SourcearXiv Machine LearningAuthor: Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband
Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration
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[Submitted on 2 Sep 2026]

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

View a PDF of the paper titled Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration, by Leonid Popryho and 2 other authors

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Abstract: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 the tetrahedral TCAD mesh and predicts, at every mesh node, the electrostatic potential together with the electron and hole quasi-Fermi levels, the fundamental unknowns of the drift-diffusion system. Training combines a data loss with finite-volume current-continuity residuals, embedding carrier-transport physics into the objective. Operating on the mesh as a graph, the surrogate inherits size generalization: a model trained on few-fin meshes applies unchanged to substantially larger arrays, bounded at inference only by GPU memory. Per-node uncertainty from a deep ensemble drives an active-learning loop that screens large candidate pools in seconds and forwards only the most informative designs for full simulation. Benchmarked against Sentaurus Device on multi-fin tri-gate FinFETs, the surrogate reproduces the three drift-diffusion fields with sub-volt per-field RMSE and reaches a per-design throughput orders of magnitude higher than the full simulator. The advantage grows with device size: on large multi-fin arrays that are prohibitively slow to simulate directly, inference still completes in under a second per device, enabling Pareto-front exploration across device scales infeasible for direct TCAD sweeps.

Comments: 9 pages, 4 figures, 2 tables. Accepted at IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2026)

Subjects:

Machine Learning (cs.LG); Hardware Architecture (cs.AR); Computational Engineering, Finance, and Science (cs.CE)

Cite as: arXiv:2609.02988 [cs.LG]

(or arXiv:2609.02988v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.02988

arXiv-issued DOI via DataCite (pending registration)

Related DOI:

https://doi.org/10.1145/3831252.3834249

DOI(s) linking to related resources

Submission history

From: Leonid Popryho [view email] [v1] Wed, 2 Sep 2026 15:15:29 UTC (642 KB)

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Key points and analysis

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Key points

  • Operates natively on TCAD tetrahedral meshes, predicting continuous fields—electrostatic potential and electron/hole quasi-Fermi levels—rather than reducing designs to scalar metrics.
  • Training combines simulation data loss with finite-volume current-continuity residuals, embedding carrier-transport physics directly into the surrogate objective.
  • Treating the mesh as a graph gives size generalization, so a model trained on few-fin devices can be applied unchanged to much larger multi-fin arrays, limited mainly by GPU memory.
  • Benchmarks on multi-fin tri-gate FinFETs reproduce Sentaurus Device fields with sub-volt per-field RMSE and achieve orders-of-magnitude higher throughput, with large-array inference under one second.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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