arXiv:2603. 29268v2 Announce Type: replace Abstract: High-density through-substrate vias (TSVs) enable 2.
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arXiv:2608. 16080v1 Announce Type: new Abstract: Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve.
The paper introduces a physics‑informed graph attention network that directly operates on the tetrahedral mesh used in TCAD simulations of FinFET devices. By predicting electrostatic potential and quasi‑Fermi levels at every mesh node and training with both data loss and finite‑volume current‑continuity residuals, the surrogate retains the underlying carrier‑transport physics while achieving size generalization. Benchmarks against Sentaurus Device show sub‑volt RMSE for the drift‑diffusion fields and a per‑design throughput that is orders of magnitude faster, enabling rapid Pareto‑front exploration of large multi‑fin arrays that would otherwise be prohibitively slow to simulate.
arXiv:2606. 07463v1 Announce Type: cross Abstract: Pre-layout design space exploration (DSE) for high-speed signal integrity (SI) analysis is often limited by the computational cost of simulations and iterative optimization algorithms within modern electronic design automation (EDA) workflows.
arXiv:2606. 15234v1 Announce Type: cross Abstract: This work presents a deterministic, machine-assisted framework for SI-compliant PCB design based on the Earth Mover's Distance (EMD).
KATOsuper is an objective‑agnostic framework that accelerates neural topology optimization by coupling neural‑reparameterized TO with a Sensitivity‑Consistent Fourier Neural Operator (SC‑FNO). It uses a forward_split architecture to ensure that sensitivities derived via automatic differentiation remain consistent with predicted objectives, enabling stable optimization. The method demonstrates significant deployment‑time speedups (15–110×) over MATLAB baselines while preserving optimality across 2D and 3D benchmark problems, including compliance and stress minimization, and supports zero‑shot extrapolation to higher resolutions.
arXiv:2607. 15560v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enforcement.