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.

翻译：本文提出一种基于机器学习的自动化射频物理综合框架，可将电路网表转换为可制造的GDSII版图。尽管近期机器学习方法在拓扑选择和参数优化方面取得成效，但由于组件模型过度简化且缺乏布线能力，这些方法无法生成可制造的版图。本框架通过三项关键创新突破这些局限：（1）基于18,210种电感几何结构、在1-100 GHz频率扫描下生成的750万个训练样本构建神经网络框架，其电感Q因子预测误差低于2%，并能通过基于梯度的快速布局优化以93.77%的成功率生成高Q值版图；（2）智能参数化单元优化器，在满足设计规则检查（DRC）的前提下缩减版图面积；（3）集成频率相关电磁间距规则与DRC感知综合的完整布局布线引擎。该神经电感模型在1-100 GHz全频段展现卓越精度，支持电磁精确的组件综合与实时推理。本框架成功为射频电路生成DRC感知的GDSII版图，标志着向自动化射频物理设计迈出重要一步。