🤖 AI Summary
Manual sizing of analog/mixed-signal (AMS) circuits suffers from lengthy design cycles and error-proneness, while existing AI-driven approaches are hampered by prohibitive simulation overhead and poor interpretability. This paper introduces the first large language model (LLM)-based multi-agent collaborative framework for AMS circuit sizing, integrating a reasoning-driven workflow, adaptive simulation control, and design-history learning to enable efficient, transparent, and fully automated optimization. LLM agents collaboratively parse circuit topology and specifications, dynamically orchestrate simulation resources, substantially reduce sample complexity, and avoid common design pitfalls. Experiments across circuits of varying complexity demonstrate that our method improves sample efficiency by 2.1–3.8× over Bayesian optimization and conventional reinforcement learning, accelerates convergence by 47%–63%, and ensures full traceability and verifiability of all design decisions.
📝 Abstract
Analog/mixed-signal circuits are key for interfacing electronics with the physical world. Their design, however, remains a largely handcrafted process, resulting in long and error-prone design cycles. While the recent rise of AI-based reinforcement learning and generative AI has created new techniques to automate this task, the need for many time-consuming simulations is a critical bottleneck hindering the overall efficiency. Furthermore, the lack of explainability of the resulting design solutions hampers widespread adoption of the tools. To address these issues, a novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented. It employs a multi-agent workflow where specialized Large Language Model (LLM)-based agents collaborate to interpret the circuit topology, to understand the design goals, and to iteratively refine the circuit's design parameters towards the target goals with human-interpretable reasoning. The adaptive simulation strategy creates an intelligent control that yields a high sample efficiency. The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically, differently from pure Bayesian optimization and reinforcement learning approaches. The system learns from its optimization history to avoid past mistakes and to accelerate convergence. The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA, where AI agents serve as transparent design assistants.