This research introduces a novel Large Language Model-based approach for optimizing analog circuit designs, potentially transforming traditional circuit sizing methods.

• Leverages LLMs to understand complex circuit relationships and performance objectives
• Eliminates the need for expert human knowledge in circuit tuning
• Provides a more efficient alternative to traditional Bayesian Optimization methods
• Creates a universal sizing optimizer applicable across different circuit types

For engineering teams, this represents a significant advancement in automating complex IC development workflows, reducing design time while potentially improving performance outcomes. This approach could democratize high-quality circuit design by encoding expert knowledge in AI systems.

LLM-USO: Large Language Model-based Universal Sizing Optimizer