AI-Enabled Design Space Discovery and End-to-end Synthesis for RFICs with Reinforcement Learning and Inverse Methods Demonstrating mm-Wave/sub-THz PAs between 30 and 120GHz

📋 论文概要

该论文提出了一种基于强化学习和逆方法的AI算法流程，用于射频集成电路（RFIC）的架构发现、电路拓扑和参数优化。旨在探索超越人类直觉的设计空间，实现从匹配网络到天线等高频电路和电磁结构的端到端自动综合。

💡 主要创新点

• 首次将强化学习与逆方法结合用于RFIC端到端合成，自动发现新颖架构和拓扑。
• 提出AI驱动的高效设计空间探索方法，能够超越传统基于模板的设计限制。
• 实现了电路与电磁结构的协同优化，涵盖匹配网络、巴伦、天线等多种高频组件。

🏷 关键词

📄 原文摘要

AI-enabled algorithmic flow for architecture discovery, circuit topology and parameter optimization for RFICs, particularly exploring design spaces beyond human intuition. RF and mmWave IC design is a complex iterative design process that involves co-design of circuits and electromagnetics (EM), including matching networks (MN), combiners, splitters, hybrids, baluns, switches, diplexers, beamforming networks, antennas, and the like. Design of such high-frequency circuits and EM structures has historically relied on intuitive and analytical approaches with starting template architectures. However, there is no reason to believe that such pre-selected topologies are close to achieving the optimal performance in the space of all possible circuit and EM topologies. Consider the design of a typical RFIC, such as a mmWave PA illustrated in Fig. 25.3.1. The

👥 作者与机构

Jonathan Zhou*1, Emir Ali Karahan*1, Sherif Ghozzy1, Zheng Liu1,2, Hossein Jalili1, Kaushik Sengupta1

Princeton University, Princeton, NJ Texas Instruments, Dallas, TX 1 2 *Equally Credited Authors (ECAs) This paper presents an