Curvilinear Sub-Resolution Assist Feature Placement Through a Data-Driven U-Net Model

In advanced semiconductor manufacturing, computational lithography, particularly sub-resolution assist features (SRAFs), is crucial for enhancing the process window. However, conventional SRAF placement methodologies are hampered by a critical trade-off between speed and pattern fidelity, and they largely fail to optimize the complex, curvilinear layouts essential for advanced nodes. This study develops a deep learning framework to replace and drastically accelerate the optical refinement of SRAF shapes. We established a large-scale dataset with coarse, binarized SRAF patterns as inputs. Ground-truth labels were generated via an Level-Set Method (LSM) optimized purely for optical performance. A U-Net convolutional neural network was then trained to learn the mapping from the coarse inputs to the optically optimized outputs. Experimental results demonstrate a dual benefit: the model provides a multi-order-of-magnitude acceleration over traditional CPU-based methods and is significantly faster than modern GPU-accelerated algorithms while achieving a final pattern fidelity highly comparable to the computationally expensive LSM. The U-Net-generated SRAFs exhibit high fidelity to the ground-truth layouts and comparable optical performance. Our findings demonstrate that a data-driven surrogate can serve as an effective alternative to traditional algorithms for SRAF optical refinement. This represents a promising approach to mitigating computational costs in mask synthesis and provides a solid foundation for future integrated optimization solutions.