Code, Not Canvas: Multi-Agent Layout Generation Beyond Vision Models

Rule-constrained chip layout generation is crucial in semiconductor manufacturing industry, providing significant resouces for technology developement and data-driven methodologies. Generative AI has become the mainstream solution for layout generation backboned with GAN, ViT or Diffussion models. These methods leverages two phase flow including squish topology generation and DRC-aware geometry filling. However, vision model-based approach is sub-optimal and lacks controllability of generated layouts. To address this, we reformulate layout generation as a coding problem which offers the user max controllability of layout generation and bridges the gap between vision-based geometry generation and LLM’s coding capability. Specifically, we deliver a multi-agent framework to write Python code to create diverse GDSII layouts following given design rule constraints. We demonstrate superior performance for layout generation over SOTA vision-based models, GPT-4o and Cursor in terms of DRC-clean pattern diversity.