PZ-Agent: A Symbolic-Engine Enhanced LLM Agent for Op-Amp Topology Inquisition

Using large language models (LLMs) for EDA is a rising research interest. In this paper, we investigate whether a symbolic tool assisted LLM agent is capable of offering circuit-level inference in the analog domain. We develop a framework to inject pole-zero (PZ) knowledge into a LLM-based multi-agent system, hoping that this PZ-agent so formed can handle Op-amp topology inquisition. We demonstrate in details that analytical knowledge in the PZ form should be properly condensed to better the reasoning capability of the PZ-agent. It turns out that, with proper incorporated knowledge, the PZ-agent is capable of answering designer’s inquiries on certain sophisticated multistage Op-amp design issues.