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

2026-05-06

AI Summary

This work addresses the ill-posed, high-dimensional inverse design problem of RF passive components from S-parameters, overcoming the limitation of existing methods that are confined to single-layer binary metal structures. We propose a multimodal conditional generative framework based on a grayscale diffusion model, which supports dual-layer copper layouts with vias (64×64 pixels, 8×8 mm), broadband operation from 1–20 GHz, sub-pixel grayscale metallization, and flexible port configurations. Physical constraints—such as feed locations—are rigorously enforced via annealed Langevin projection with hard encoding. Coupled with an S-parameter surrogate model and multimodal conditional embeddings, the framework generates manufacturable designs in seconds, achieving a weighted mean absolute error of 0.77 ± 1.28 dB between surrogate predictions and target S-parameters. Fabrication and measurement of two RO4003C-based devices—a manufacturable hairpin filter alternative and a comb-line bandpass filter designed solely from S-parameters—demonstrate the method’s effectiveness and practicality.

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 $8\times8$ mm board discretized at $64\times64$ 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 $0.77 \pm 1.28$ 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.

Problem

• inverse design
• RF passives
• S-parameters
• multi-layer
• sub-pixel resolution

Innovation

• inverse design
• grayscale diffusion
• sub-pixel metallization
• S-parameter conditioning
• multi-layer RF passives