CASCoDe: A Closed-Loop Agentic Framework for Analog Sizing and Layout Co-Design
Ziming Wei, Bingyang Liu, David Z. Pan
原文摘要
Expert analog layouts differ from auto-generated ones in density and regularity, not only in legality: experts make a succession of discrete, hierarchical physical decisions and revisit each after extracted electrical results, while most existing automated flows act on a physical search space fixed before the controller runs. We present CASCoDe, a closed-loop LLM agent framework for analog netlist-to-layout design that formalizes those expert decisions as a hierarchical discrete action space-sizing selection, structural floorplan, local layout refinement, routing-attempt selection-on which the agent runs feedback-driven tree search from a circuit netlist to a verified GDS. The agent authors the circuit-specific floorplans and scripts, and an execution layer of interface adaptations over adapted tools make every action land at physical implementation and be judged by DRC, LVS, and extracted-RC simulation. On 20 SKY130 amplifiers spanning single-, two-, and three-stage topologies, CASCoDe is the only flow that delivers a DRC- and LVS-clean routed layout when meeting design specifications for every circuit. It reduces area by 59-85% (mean 71%) against a conventional P&R flow on identical PCells, raises mean device density from 0.39 to 0.74 against APPlace, MAGICAL, ALIGN and ICPI. A layout that beats every baseline in area and legality appears after 11 minutes of agent time on average, and ablations show that the structured action space and the feedback-guided tree search each reduce area and search cost.
cs.AI