LayoutCopilot: LLM-Empowered Analog Layout Design towards Enhanced Human-Machine Interaction

Analog and mixed-signal circuits are crucial for interfacing digital systems with the real world, yet the layout design remains manual and highly labor-intensive. Fully automated tools for layout design have made significant progress in easing this burden, but they often restrict flexibility and designer control. Interactive design flows combine the strengths of both manual and automated design; however, designers still face challenges in human-machine interaction, such as complex command sets and manual code writing. In this paper, we introduce LayoutCopilot, an LLM-empowered interactive layout design framework that addresses this challenge by enabling the translation of high-level design intents expressed in natural language into actionable commands. It also incorporates automated constraint extraction, reducing repetitive tasks and enhancing interaction between designers and the tool. Our experiments demonstrate that this framework undergoes validation for syntactic and functional correctness and is successfully applied to real-world analog design tasks, from constraint extraction to layout refinement, achieving efficient designers’ involvement with reduced manual efforts.