AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled technologies. Although recent large language model (LLM)-based methods show promise in improving sample efficiency and interpretability, existing approaches often lack explicit circuit-topology understanding and are mainly evaluated on relatively simple analog building blocks. This paper presents a multi-agent LLM-based framework for complex analog circuit sizing. The proposed framework first analyzes the circuit topology and decomposes the netlist into functional blocks and substructures. It also extracts lightweight...
| Authors | Yijia Hao, Pratibha Verma, Dongxu Guo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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