论文标题
自动模拟/射频电路参数优化的域知识深度学习
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization
论文作者
论文摘要
模拟电路的设计自动化是一个长期的挑战。本文提出了通过图形学习增强的增强学习方法，以在延期前阶段自动化模拟电路参数优化，即查找设备参数以满足所需的电路规格。与所有先前的方法不同，我们的方法是受人类专家的启发，他们依靠模拟电路设计的领域知识（例如电路拓扑和电路规格之间的耦合）来解决问题。通过将这种关键领域知识通过多模式网络纳入政策培训中，该方法可以最佳地了解电路参数和设计目标之间的复杂关系，从而在优化过程中实现最佳决策。示例性电路的实验结果表明，它达到了人类水平的设计准确性（99％）1.5倍现有表现最佳方法的效率。我们的方法还显示出更好的概括能力，可以在电路性能优化中看不见的规格和最佳性。此外，它适用于在新兴半导体技术上设计射频电路，从而打破了设计常规模拟电路时先前学习方法的局限性。
The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a multimodal network, the method best learns the complex relations between circuit parameters and design targets, enabling optimal decisions in the optimization process. Experimental results on exemplary circuits show it achieves human-level design accuracy (99%) 1.5X efficiency of existing best-performing methods. Our method also shows better generalization ability to unseen specifications and optimality in circuit performance optimization. Moreover, it applies to design radio-frequency circuits on emerging semiconductor technologies, breaking the limitations of prior learning methods in designing conventional analog circuits.
