ForgePattern: Flexible Layout Pattern Generation through Reinforcement Learning

Layout pattern generation provides dataset support to numerous Design for Manufacturability (DFM) studies, with different research objectives requiring distinct styles of layout patterns. However, existing layout pattern generation methods only support random generation or limited conditional generation based on image-text pairs annotated for specific tasks, which fails to meet the diverse requirements of downstream tasks. To address this limitation, we propose ForgePattern, a flexible layout pattern generation framework that accommodates diverse task-specific requirements through adaptive selection among customizable reward functions. ForgePattern leverages a customized diffusion model as the generative backbone and incorporates reinforcement learning to directly optimize the diffusion model. By maximizing the expected reward of the reinforcement learning objective, ForgePattern aligns the generated layout patterns with specific requirements without requiring additional data collection or human annotation. Experimental results on challenging layout pattern generation tasks demonstrate that ForgePattern can effectively generate high-quality layout patterns while meeting diverse customization requirements.