AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents
Summary: arXiv:2603.23910v1 Announce Type: new
Recent advancements in large language models (LLMs) have opened new avenues for the automation of analog circuit design. Traditional LLM-based methods often rely on a linear process of generation, diagnosis, and correction, which tends to prioritize succinct summaries over in-depth, domain-specific insights. This single-model approach suffers from what is known as context attrition, a phenomenon where critical technical details are lost over time during the generative process.
Introducing AnalogAgent
To tackle these challenges, researchers have proposed a groundbreaking framework called AnalogAgent. This innovative system is designed to automate analog circuit design without the need for extensive training. AnalogAgent employs a multi-agent system (MAS) powered by LLM technology, coupled with a self-evolving memory (SEM) mechanism. This unique combination allows AnalogAgent to significantly improve the automation process.
How AnalogAgent Works
AnalogAgent operates by coordinating three key components:
- Code Generator: This component is responsible for the initial creation of circuit designs based on specified parameters.
- Design Optimizer: This agent refines the generated designs, ensuring they meet functional and performance criteria.
- Knowledge Curator: This aspect distills execution feedback to enhance the system’s adaptive playbook stored in SEM, thus facilitating ongoing improvements.
Benefits of AnalogAgent
The integration of these components allows for targeted guidance during subsequent generations, thus enabling cross-task transfer without the need for additional expert feedback, databases, or libraries. As a result, AnalogAgent achieves impressive performance metrics across established benchmarks:
- 92% Pass@1: Achieved with the advanced Gemini model.
- 97.4% Pass@1: Reached with the newly unveiled GPT-5 model.
- +48.8% Average Pass@1 Gain: Notably observed with compact models such as Qwen-8B, leading to a total of 72.1% Pass@1 overall.
Conclusion
The development of AnalogAgent marks a significant advancement in the field of analog circuit design automation. By leveraging the capabilities of LLMs within a multi-agent framework, it not only overcomes the limitations of traditional approaches but also enhances the quality and efficiency of the design process. As this technology continues to evolve, it holds the potential to revolutionize how engineers and designers approach analog circuit design, paving the way for more sophisticated electronic systems in the future.