With the development of semiconductor manufacturing processes, critical dimension small-angle x-ray scattering (CDSAXS) has been identified as a potential solution for measurement. It is worthy of exploring how to achieve fast parameter extraction. In this paper, we propose a XSCNN model based on deep learning to reconstruct the parameters related to structure and measurement conditions. Simulation experiments performed on a trapezoidal grating have demonstrated that XSCNN can produce satisfactory results. It is expected that deep learning will provide a practical solution in CD-SAXS.
The semiconductor industry’s device dimensions continue shrinking and device architectures increase in 3D complexity, while incorporating new materials. To keep pace with these changes, new critical in-line metrology accurately and efficiently evaluating the structural profiles will be needed. Small angle X-ray scatterometry shows promise to be considered for critical dimension (CD) metrology for future nodes. In this paper, we report simulation results of the transmission small angle X-ray scattering (T-SAXS) metrology to evaluate its measurement capability for 3D periodic architectures. Based on measurability analysis for various 3D structural models, T-SAXS shows a good potential solution to the future 3D architectures measurement.
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