Inverse lithography based on a physics-driven deep learning approach

Optical proximity correction (OPC) is essential for advanced semiconductor manufacturing beyond 45nm node by improving lithography resolution. However, traditional OPC techniques are limited by scalar imaging models and gradient-based algorithms. This study introduces a physics-driven deep learning OPC scheme that leverages vector imaging model. Guided by inverse lithography technology (ILT), the algorithm requires no extensive datasets, relying instead on varied, randomly generated patterns for training. The results from numerical testing confirm the approach's accuracy and efficiency.