LLM-SRAF: Sub-Resolution Assist Feature Generation Using Large Language Model
Tianyi Li, Zhexin Tang, Tao Wu, Bei Yu, Jingyi Yu, Hao Geng · 2025
As integrated circuit (IC) feature sizes continue to shrink, using sub-resolution assist features (SRAF) becomes increasingly crucial for improving wafer pattern resolution and fidelity. However, model-based SRAF insertion techniques, while accurate, require substantial computational resources and are often impractical for industrial scenarios. This demands more efficient and industry-compatible methods that maintain high performance. In this work, we introduce LLM-SRAF, a novel framework for SRAF generation driven by a large language model fine-tuned on an SRAF dataset. LLM-SRAF accepts semantic prompt inputs, including SRAF generation task descriptions, OPC recipe, lithography conditions, mask rules, and sequential layout descriptions, to directly generate SRAFs. Both supervised fine-tuning and reinforcement learning with human feedback (RLHF) are employed to enable the model to acquire domain-specific knowledge and specialize in SRAF generation. Experimental results show that LLM-SRAF outperforms existing state-of-the-art methods in metrics of mask quality, including edge placement error (EPE) and process variation band (PVB) area. Moreover, the runtime of LLM-SRAF is also 3x faster compared to the Calibre commercial tool.