用神经网络实现射频电路自动布局布线，精准预测电感性能并生成可制造版图。
EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits
- 训练超18万电感样本，神经网络预测电感品质因数误差<2%。
- 93.77%成功率生成高Q值版图，支持实时梯度优化。
- 融合电磁场模型与设计规则，适合射频集成电路自动化设计。
本文提出一种基于机器学习的射频电路物理综合框架，可将电路网表自动转化为可制造的GDSII版图。现有机器学习方法虽在拓扑选择和参数优化上表现良好，但因组件模型过于简化且缺乏布线能力，无法生成可制造版图。本框架通过三项创新克服此局限：(1) 基于18,210个电感几何结构及1-100 GHz频率扫描数据，生成750万训练样本的神经网络，预测电感品质因数误差低于2%，支持快速梯度优化，成功生成高Q值版图率达93.77%；(2) 智能P-Cell优化器在保持设计规则检查（DRC）合规的前提下缩小版图面积；(3) 具备频率相关电磁间距规则和DRC感知的完整布局布线引擎。神经电感模型在1–100 GHz范围内表现出色，实现电磁精度的组件合成与实时推理。该框架成功生成符合DRC要求的射频电路GDSII版图，标志着自动化射频物理设计的重要进展。
原文摘要 · Abstract (English)
This paper presents an ML-driven framework for automated RF physical synthesis that transforms circuit netlists into manufacturable GDSII layouts. While recent ML approaches demonstrate success in topology selection and parameter optimization, they fail to produce manufacturable layouts due to oversimplified component models and lack of routing capabilities. Our framework addresses these limitations through three key innovations: (1) a neural network framework trained on 18,210 inductor geometries with frequency sweeps from 1-100 GHz, generating 7.5 million training samples, that predicts inductor Q-factor with less than 2% error and enables fast gradient-based layout optimization with a 93.77% success rate in producing high-Q layouts; (2) an intelligent P-Cell optimizer that reduces layout area while maintaining design-rule-check (DRC) compliance; and (3) a complete placement and routing engine with frequency-dependent EM spacing rules and DRC-aware synthesis. The neural inductor model demonstrates superior accuracy across 1-100 GHz, enabling EM-accurate component synthesis with real-time inference. The framework successfully generates DRC-aware GDSII layouts for RF circuits, representing a significant step toward automated RF physical design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。