用分层推理树模拟人类设计思维，提升模拟电路优化效率与适应性。
HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization
- 基于分层电路推理树构建智能体框架，模仿人类设计逻辑。
- 在40个电路基准上，子电路与回路的F1指标分别提升超13.5%和37.8%。
- 支持拓扑重构与参数调整，加速3倍以上适应新设计规范。
传统AI驱动的模拟电路设计自动化方法受限于高质量数据依赖、跨架构迁移能力差以及缺乏自适应机制。本文提出HeaRT——一种基于分层电路推理树的智能体框架，用于自动化设计循环，迈向具备自适应能力的人类式设计优化。在包含40个电路的平坦化SPICE网表基准测试中，无论模型骨架如何，HeaRT在少样本提示下均使子电路的F1提升≥13.5%，回路的F1提升≥37.8%，且在电路复杂度增加时仍保持优势。实验进一步表明，面对不同优化策略下的规格变化，HeaRT在增量设计适配任务中实现≥3倍加速，支持拓扑重构与尺寸调整。
原文摘要 · Abstract (English)
Conventional AI-driven AMS design automation algorithms remain constrained by their reliance on high-quality datasets to capture underlying circuit behavior, coupled with poor transferability across architectures, and a lack of adaptive mechanisms. This work proposes HeaRT, a hierarchical circuit reasoning-based agentic framework for automation loops and a step toward adaptive, human-style design optimization. HeaRT consistently improves F1(subcircuits) by >= 13.5% and F1(loops) by >= 37.8% over few-shot prompting baselines across multiple LLM backbones on our 40-circuit AMS benchmark of flattened SPICE netlists, even as circuit complexity increases. Our experiments further show that HeaRT achieves >= 3x faster convergence in incremental design adaptation tasks under specification shifts across diverse optimization approaches, supporting both topology reconfiguration and sizing.
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