Title: A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction Show Chinese title

Title: 基于大型语言模型的多智能体框架用于模拟电路尺寸关系提取

Abstract: In the design process of the analog circuit pre-layout phase, device sizing is an important step in determining whether an analog circuit can meet the required performance metrics. Many existing techniques extract the circuit sizing task as a mathematical optimization problem to solve and continuously improve the optimization efficiency from a mathematical perspective. But they ignore the automatic introduction of prior knowledge, fail to achieve effective pruning of the search space, which thereby leads to a considerable compression margin remaining in the search space. To alleviate this problem, we propose a large language model (LLM)-based multi-agent framework for analog circuits' sizing relationships extraction from academic papers. The search space in the sizing process can be effectively pruned based on the sizing relationship extracted by this framework. Eventually, we conducted tests on 3 types of circuits, and the optimization efficiency was improved by $2.32 \sim 26.6 \times$. This work demonstrates that the LLM can effectively prune the search space for analog circuit sizing, providing a new solution for the combination of LLMs and conventional analog circuit design automation methods. Show Chinese abstract

Abstract: 在模拟电路预布局阶段的设计过程中，器件尺寸确定是决定模拟电路是否能满足所需性能指标的重要步骤。 许多现有技术将电路尺寸确定任务提取为数学优化问题来求解，并从数学角度持续提高优化效率。 但它们忽略了先验知识的自动引入，未能实现对搜索空间的有效剪枝，从而导致搜索空间中仍存在较大的压缩余地。 为缓解这一问题，我们提出了一种基于大语言模型（LLM）的多智能体框架，用于从学术论文中提取模拟电路的尺寸关系。 通过该框架提取的尺寸关系，可以有效剪枝尺寸确定过程中的搜索空间。 最终，我们在3种电路类型上进行了测试，优化效率提高了$2.32 \sim 26.6 \times$。 这项工作表明，LLM能够有效剪枝模拟电路尺寸确定的搜索空间，为大语言模型与传统模拟电路设计自动化方法的结合提供了一种新解决方案。