UNeXt-ILT: fast and global context-aware inverse lithography solution

BackgroundDue to the limitations of optimization degrees of freedom in traditional optical proximity correction, it cannot meet the mask optimization requirements of advanced technology nodes. Inverse lithography technology (ILT) is considered the most promising resolution enhancement technique, and the trade-off between mask optimization quality and computation time is a challenge.AimThe biggest limitation of ILT is its high computational complexity, which requires exploring an ILT algorithm that can ensure the fidelity of lithography patterns and the process variation (PV) band while also having a short computation time.ApproachWe propose UNeXt-ILT, a deep learning–based ILT technology. The UNeXt model is adopted as the backbone model, and its multi-layer perceptron structure ensures the lightweight of the model while having global context-awareness capability, thus quickly providing a high-quality initial mask and accelerating the overall computation time. In addition, the addition of mask regularization and mask filtering techniques enhances the robustness of gradient descent–based ILT algorithms and further improves the quality of mask optimization.ResultsCompared with the most advanced deep learning–based ILT algorithm, UNeXt-ILT reduces L2 error by 17.83%, reduces PV band by 8.76%, and shortens turnaround time by 34.48%.ConclusionsWe contribute to improving the robustness and computational speed of the ILT algorithm, thereby promoting its wider application.