EM-Aware Physical Synthesis: Neural Inductor Modeling and Intelligent Placement&Routing for RF Circuits

2026-02-12

AI Summary

Existing machine learning approaches struggle to generate manufacturable RF GDSII layouts due to oversimplified component models and the absence of routing capabilities. This work proposes the first machine learning–driven physical synthesis framework tailored for RF circuits, integrating a high-fidelity neural inductor model trained on 18,210 structures and 7.5 million samples, a DRC-aware intelligent P-Cell optimizer, and a placement-and-routing engine that enforces frequency-dependent electromagnetic spacing rules. The framework enables co-optimization of electromagnetic awareness and design rule compliance. Experimental results demonstrate inductor Q-factor prediction errors below 2%, a 93.77% success rate in generating high-Q layouts, and successful production of DRC-clean GDSII outputs, with real-time inference support across the 1–100 GHz range.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• RF physical synthesis
• manufacturable layout
• inductor modeling
• placement and routing
• EM-aware design

Innovation

• neural inductor modeling
• intelligent placement and routing
• EM-aware synthesis
• RF physical design automation
• DRC-compliant layout