Fast extreme ultraviolet lithography mask near-field calculation method based on machine learning

Near-field calculation for a three-dimensional (3D) mask is a fundamental task in extreme ultraviolet(EUV) lithography simulations. This paper develops a fast3Dmask near-field calculation method based on machine learning for EUV lithography. First, the training libraries of rigorous mask near fields are built based on a set of representative mask samples and reference source points. In the testing stage, the mask under consideration is first segmented into a set of non-overlapped patches. Then the local near field of each patch is calculated based on the non-parametric regression and data fusion techniques. Finally, the entire mask near field is synthesized based on the image stitching and data fitting methods. The proposed method is shownto achieve higher accuracy compared to the traditional domain decomposition method. In addition, the computational efficiency is improved up to an order of magnitude compared to the rigorous electromagnetic field simulator.