AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

Analog circuit design has long been a cumbersome process, reliant on expert intuition to navigate complex design spaces. Recent advancements in large language models (LLMs) have introduced a novel approach, leveraging natural language reasoning to streamline circuit design tasks. A new framework, AaLLM, promises to revolutionize this process by providing an end-to-end solution that encompasses both topology generation and sizing. This integrated approach has the potential to significantly reduce design time and increase efficiency. By harnessing the power of LLMs, AaLLM can generate and optimize analog circuit designs with greater speed and accuracy than traditional methods¹. The implications of this technology extend far beyond the realm of circuit design, with potential impacts on policy, security, and workforce dynamics. As AI continues to advance, the ability to automate complex design tasks will have significant repercussions for industries reliant on analog circuit design, making it essential for practitioners to stay informed about these developments.