PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation

Abstract

Traditional design of analog circuits heavily relies on manual interventions across topology, sizing, and layout, with prior automation addressing stages in isolation. In this work, we propose PANDA, an LLM-enhanced framework that bridges high-level design intent to final layout by actively managing cross-stage dependencies through guided topology synthesis, substructure-aware sizing, and constraint-driven layout generation. This shifts automation from algorithm-centric execution to intent-centric co-design, reducing turnaround time from days or weeks to hours while improving design performance. PANDA is released at https://github.com/PKU-IDEA/PANDA.

Type
Publication
In Proceedings of the 63rd ACM/IEEE Design Automation Conference
Xiaohan Gao 高笑涵
Researcher

My research interests include optimization algorithms, robotics, and video gaming. My previous research mainly focuses on AI for chip design.