Reproduce “MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Design” on OpenResearch
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About this paper
Pin-Han Chen, Yu-Sheng Lin, Wei-Cheng Lee, Tin-Yu Leu, Po-Hsiang Hsu, Anjana Dissanayake, Sungjin Oh, Chinq-Shiun Chiu
MenTeR is a fully-automated multi-agent workflow developed by MediaTek for end-to-end RF/Analog circuit netlist design, leveraging specialized AI agents and techniques like Diagram-Aware Retrieval-Augmented Generation (DA-RAG) and Chain-of-Stage (CoS) reasoning. It achieved an 84.2% Pass@1 rate on complex analog design tasks and demonstrated superior performance and token efficiency compared to single-agent approaches, successfully implementing industrial-level circuits such as Bandgap References and PLLs.
Key insights
- Integrating visual information processing via DA-RAG is crucial for LLM effectiveness in specialized engineering domains like RF/Analog design, which heavily rely on diagrams.
- Structured, multi-stage reasoning through CoS significantly improves LLM performance and efficiency for complex, hierarchical design problems by managing retrieved knowledge and prioritizing design constraints.
- A robust multi-agent architecture with specialized, collaborative AI agents enables more effective, scalable, and reliable end-to-end analog circuit netlist generation compared to single-agent approaches.
Summary by alphaXiv.
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