K-TRAIL：模拟器引导的电磁/射频电路生成式设计

中文摘要 AI 辅助

射频和电磁（EM）电路的逆向设计具有挑战性，因为电路布局与电响应之间的关系并非唯一，且全波仿真计算成本高昂。本文提出K-TRAIL，一种用于自动化EM/RF电路综合的模拟器引导生成框架。K-TRAIL将基于扩散的布局生成与无导数集成卡尔曼引导相结合，允许在生成过程中利用黑盒EM模拟器的反馈来优化候选布局，而无需伴随灵敏度或可微分求解器模型。该框架支持从指定的S参数响应进行综合，也支持直接从射频性能约束进行综合。在多层层射频集成电路（RFIC）结构上的实验表明，模拟器引导的生成能改善与目标响应的一致性，并能识别出满足电路级设计要求的结构上不同的布局。所提出的方法为生成式、验证感知的射频电路设计提供了一条实用路径，同时保留了探索多样化布局拓扑的灵活性。

英文摘要

Inverse design of RF and electromagnetic (EM) circuits is challenging because the relationship between circuit layout and electrical response is non-unique, and full-wave simulation is computationally expensive. This paper presents K-TRAIL, a simulator-guided generative framework for automated EM/RF circuit synthesis. K-TRAIL combines diffusion-based layout generation with derivative-free ensemble Kalman guidance, allowing feedback from a black-box EM simulator to refine candidate layouts during generation without requiring adjoint sensitivities or differentiable solver models. The framework supports both synthesis from prescribed S-parameter responses and synthesis directly from RF performance constraints. Experiments on multi-layer RFIC structures show that simulator-guided generation improves agreement with target responses and can identify structurally distinct layouts that satisfy circuit-level design requirements. The proposed approach provides a practical path toward generative, verification-aware RF circuit design while retaining the flexibility to explore diverse layout topologies.

发表机构

• Oregon State University（俄勒冈州立大学）
• Yale University（耶鲁大学）

机构由 AI 辅助整理，请以论文原文为准。