Simulator-Refined Diffusion for Radio-Frequency Inverse Design

2026-09-29

AI Summary

This study addresses the challenge of satisfying quantitative electromagnetic specifications when applying diffusion models to PCB inverse design. To this end, it proposes Simulation-guided Reverse Diffusion (SRD), a method whose core innovation lies in integrating a low-fidelity differentiable surrogate with a high-fidelity non-differentiable full-wave simulator to optimize the sampling process. Specifically, gradients from the surrogate model provide directional guidance, while the simulator performs precise searches along these directions to update samples, replacing conventional stochastic perturbation strategies. Experimental results demonstrate that SRD improves S-parameter matching accuracy by approximately 21.2% compared to state-of-the-art methods. This work establishes a new paradigm for generative design under physical constraints.

Abstract

Diffusion models have shown potential in inverse design of printed circuit boards (PCBs), enabling the generation of layouts conditioned on target S-parameters. Despite this promise, applying diffusion models to PCB layout generation remains challenging due to their difficulty in meeting the quantitative electromagnetic specifications. A common approach is gradient-based guidance, which biases the diffusion sampling process with the gradient of an objective used for evaluation. However, full-wave electromagnetic simulators are accurate but expensive and typically non-differentiable, whereas differentiable surrogates are informative but not always reliable. To address these limitations, this paper proposes Simulator-Refined Diffusion (SRD), a novel combination of a low-fidelity differentiable surrogate and a high-fidelity non-differentiable simulator within the diffusion sampling process. Unlike standard zeroth-order optimization, which requires a great number of random perturbations, our approach uses the surrogate's gradient to propose the perturbation direction while the simulator then searches based on this direction to identify an effective design update. Experimental results across different settings show that this method consistently outperforms current state-of-the-art methods, producing layouts whose simulated S-parameters match the target specifications up to 21.2% closer for in-distribution targets and up to 19.8% for out-of-distribution targets.

Problem

• inverse design
• diffusion models
• printed circuit boards
• electromagnetic simulation
• S-parameters

Innovation

• Diffusion Models
• Inverse Design
• Simulator-Refined Diffusion
• Differentiable Surrogate
• Electromagnetic Simulation