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Inverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter Conditioning

Signal Processing 2026-05-12 v1 Machine Learning

Abstract

Inverse design of RF passive components from S-parameters is a high-dimensional, ill-posed problem, and prior generative approaches are limited to single-layer binary-metallization structures. This paper presents an inverse design approach that generates passive components from partial S-parameter inputs on an 8×88\times8 mm board discretized at 64×6464\times64 pixels with sub-pixel grayscale metallization across 1-20 GHz. The framework generates two-layer copper layouts with vias, with hard physical constraints on feed locations enforced through annealed Langevin projection, flexible multi-modal conditioning on partial S-parameter specifications, port locations, dielectric properties, reference topology, and variable port placement. Candidate designs are generated in seconds, with surrogate-predicted S-parameters matching targets to within 0.77±1.280.77 \pm 1.28 dB weighted mean absolute error. We validate the approach with two fabricated designs on RO4003C: a manufacturable alternative to a hairpin filter whose coupling gaps violate fabrication rules, and a combline bandpass filter designed from scratch given only target S-parameters.

Keywords

Cite

@article{arxiv.2605.08233,
  title  = {Inverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter Conditioning},
  author = {Tommaso Dreossi and Christopher M. Bryant and Hao Liu and Nathan Mirman and Noah Kessler and Michael Frei and Harish Krishnaswamy},
  journal= {arXiv preprint arXiv:2605.08233},
  year   = {2026}
}
R2 v1 2026-07-01T12:58:34.843Z