面向LLM辅助的模拟电路版图优化的感知仿真上下文内策略改进
Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement
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中文总结 AI 辅助
针对LLM辅助模拟版图优化的样本效率问题，提出感知仿真的LLM多智能体框架，通过上下文内策略改进，仅需数十次仿真即可提升后版图性能，优于生成器启发式规则与贝叶斯优化方法。
中文摘要 AI 辅助
模拟集成电路版图设计仍是以仿真驱动优化为主的劳动密集型迭代过程。尽管端到端版图生成器能加快初始布局布线，但仍需专家针对严格的设计规范，通过反复的后版图仿真手动调整版图优化参数。贝叶斯优化（BO）虽广泛用于模拟集成电路设计的参数调优，但在版图层面通常需数百至数千次评估，每次评估都涉及高成本的寄生参数提取和后版图仿真，导致其实用性受限。近期，大语言模型（LLM）在提升这类仿真驱动调优的样本效率方面展现出潜力，但其对几何版图上下文和设计特定启发式规则的访问受限，限制了其操控版图优化过程的能力。本文提出一种感知仿真的LLM多智能体框架，该框架通过在紧凑结构化版图表示上执行“行动-观察-反思”循环，迭代更新模拟版图生成器提供的版图优化参数，从而实现上下文内策略改进（ICPI）。对真实模拟电路的实验表明，仅需数十次后版图仿真，该方法就能使后版图性能优于生成器内置启发式规则和基于BO的调优方法。
英文摘要
Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement. Although end-to-end layout generators accelerate initial placement and routing, they still require experts to manually tune layout optimization parameters with repeated post-layout simulations for stringent design specifications. While Bayesian Optimization (BO) is widely adopted for parameter tuning in analog IC design, at the layout level it typically requires hundreds to thousands of evaluations, each involving costly parasitic extraction and post-layout simulation, which makes it impractical. Recently, Large Language Models (LLMs) have demonstrated potential in improving the sample efficiency of such simulation-driven tuning. However, their restricted access to geometric layout context and design-specific heuristics limits their ability to manipulate the layout optimization process. In this paper, we propose a simulation-aware LLM multi-agent framework that performs in-context policy improvement (ICPI) by iteratively updating layout optimization parameters exposed by an analog layout generator through an act-observe-reflect loop on compact structured layout representations. Experiments on real-world analog circuits show that, with only tens of post-layout simulations, our approach improves post-layout performance over the generator's built-in heuristics and BO-based tuning method.
发表机构
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。
