AutoSizer: 通过大语言模型代理自动调整模拟和混合信号电路的尺寸
AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents
AI总结：
提出AutoSizer，一种反射式LLM驱动的元优化框架，通过双循环结构统一电路理解、自适应搜索空间构建和优化编排，在模拟和混合信号电路尺寸调整中实现更优解质量、更快收敛和更高成功率。
摘要：
模拟和混合信号（AMS）集成电路的设计仍然严重依赖专家知识，其中晶体管尺寸调整由于非线性行为、高维设计空间和严格的性能约束而成为主要瓶颈。现有的电子设计自动化（EDA）方法通常将尺寸调整视为静态黑箱优化，导致解决方案效率低下且鲁棒性不足。尽管大语言模型（LLM）展现出强大的推理能力，但它们并不适合AMS尺寸调整中的精确数值优化。为弥补这一差距，我们提出AutoSizer，一种反射式LLM驱动的元优化框架，以闭环方式统一电路理解、自适应搜索空间构建和优化编排。它采用双循环优化框架，内循环负责电路尺寸调整，外循环分析优化动态和约束，从仿真反馈中迭代优化搜索空间。我们进一步引入AMS-SizingBench，一个包含SKY130 CMOS技术中24种不同AMS电路的开源基准，旨在评估在基于仿真器的现实约束下的自适应优化策略。实验表明，AutoSizer在不同电路难度下实现了更高的解质量、更快的收敛速度和更高的成功率，优于传统优化方法和现有的基于LLM的代理。
英文摘要：
The design of Analog and Mixed-Signal (AMS) integrated circuits remains heavily reliant on expert knowledge, with transistor sizing a major bottleneck due to nonlinear behavior, high-dimensional design spaces, and strict performance constraints. Existing Electronic Design Automation (EDA) methods typically frame sizing as static black-box optimization, resulting in inefficient and less robust solutions. Although Large Language Models (LLMs) exhibit strong reasoning abilities, they are not suited for precise numerical optimization in AMS sizing. To address this gap, we propose AutoSizer, a reflective LLM-driven meta-optimization framework that unifies circuit understanding, adaptive search-space construction, and optimization orchestration in a closed loop. It employs a two-loop optimization framework, with an inner loop for circuit sizing and an outer loop that analyzes optimization dynamics and constraints to iteratively refine the search space from simulation feedback. We further introduce AMS-SizingBench, an open benchmark comprising 24 diverse AMS circuits in SKY130 CMOS technology, designed to evaluate adaptive optimization policies under realistic simulator-based constraints. AutoSizer experimentally achieves higher solution quality, faster convergence, and higher success rate across varying circuit difficulties, outperforming both traditional optimization methods and existing LLM-based agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
