AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
By: Yao Lai , Souradip Poddar , Sungyoung Lee and more
Potential Business Impact:
Computers design electronic circuits automatically and perfectly.
Despite recent advances, analog front-end design still relies heavily on expert intuition and iterative simulations, which limits the potential for automation. We present AnalogCoder-Pro, a multimodal large language model (LLM) framework that integrates generative and optimization techniques. The framework features a multimodal diagnosis-and-repair feedback loop that uses simulation error messages and waveform images to autonomously correct design errors. It also builds a reusable circuit tool library by archiving successful designs as modular subcircuits, accelerating the development of complex systems. Furthermore, it enables end-to-end automation by generating circuit topologies from target specifications, extracting key parameters, and applying Bayesian optimization for device sizing. On a curated benchmark suite covering 13 circuit types, AnalogCoder-Pro successfully designed 28 circuits and consistently outperformed existing LLM-based methods in figures of merit.
Similar Papers
AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
Machine Learning (CS)
Designs computer parts automatically and better.
LLMs for Analog Circuit Design Continuum (ACDC)
Machine Learning (CS)
Helps computers design circuits, but they make mistakes.
Enhancing Large Language Models for End-to-End Circuit Analysis Problem Solving
Computers and Society
Helps computers solve tricky electric circuit problems.