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arXiv Machine Learning By Yuhao Mao, Chenhao Chu, Martin Vechev, Hua Wang

End-to-End Neural Modeling of EM Response and Design Performance for Free-Form RFIC Passives

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The paper presents an end-to-end neural framework that predicts electromagnetic (EM) response and design performance for free-form RFIC passive matching networks across varying frequencies and circuit contexts. It combines a frequency-conditioned neural simulator with a circuit-conditioned neural solver, outputting both explicit EM response and a complementary response to capture orthogonal information. The 21‑million‑parameter model, trained on a large dataset of expert‑designed and random passive layouts, achieves $R^2>0.98$ for most targets and is orders of magnitude faster than traditional EM simulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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