具有电磁意识的物理综合：神经感应器建模和智能放置与路由用于射频电路
EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement & Routing for RF Circuits
AI总结：
本文提出一种基于神经网络的射频电路综合方法，通过高精度电感器建模和智能布局优化，实现电磁准确的可制造GDSII布局生成。
摘要：
本文提出了一种基于机器学习的自动化射频物理综合框架，将电路网络列表转换为可制造的GDSII布局。尽管最近的机器学习方法在拓扑选择和参数优化方面表现出色，但它们由于组件模型过于简化和缺乏路由能力而无法生成可制造的布局。我们的框架通过三个关键创新解决了这些限制：（1）一个在1-100 GHz频率扫描中训练了18,210个电感器几何形状，生成750万个训练样本的神经网络框架，能够以小于2%的误差预测电感器的Q因子，并通过93.77%的成功率生成高Q布局的快速梯度基于布局优化；（2）一个智能P-Cell优化器，减少布局面积的同时保持设计规则检查（DRC）合规性；（3）一个完整的放置和路由引擎，具有频率相关的电磁间距规则和DRC-aware综合。神经感应器模型在1-100 GHz范围内表现出卓越的准确性，能够实现电磁准确的组件综合和实时推断。该框架成功地为射频电路生成DRC-aware的GDSII布局，代表着自动化射频物理设计的重要一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
