3D mask defect and repair simulation based on SEM images

Background: In practice, manufactured lithography masks come with a certain number of unintended defects. Therefore, mask fabrication is accompanied by a subsequent repair, performed via etching or material deposition by a gas-assisted focused electron beam. Aim: The goal of this work is to assess the lithographic impact of mask defects and corresponding repair by simulations. Approach: For this purpose, a novel analytical method was developed to retrieve exact repair shapes f rom scanning electron microscope (SEM) images of the mask patterns. A developed method, based on computer vision and image processing, is combined with a dedicated artificial intelligence (AI) network trained to detect defective contact and line/space patterns from mask SEM images. Lithography simulations were done for 3D masks derived from the real SEM images. Results: 3D masks with the 13 nm lines and 18 nm contact holes are simulated, and corresponding aerial images are computed. Different typical defects are investigated and demonstrate the robustness and effectiveness of the developed software. Conclusions: The developed analytical algorithm demonstrates a stable and accurate extraction of repair shapes from given mask SEM images. Using our simulation procedure, the impact of each defect from a variety of SEM images was assessed, and lithographic performance after a repair was predicted. In the simulations, the determination of the optimum repair shape is implemented as a two-step procedure providing a large overlap of process windows of defect-free and repaired features, hence high-quality lithography output.