In X-ray critical dimension metrology (XCD), it is a common practice to select an appropriate measurement configuration, including incidence angle, azimuth angle, exposure time, etc., to improve measurement results. This is crucial as the quality of the obtained signature is impacted not just by the instrument's precision, but also by the specific chosen measurement configuration. This is known as the measurement configuration optimization (MCO) problem. This paper proposes a general MCO method based on the theory of error propagation and error estimation techniques using condition numbers. Finally, the MCO problem in XCD is framed as optimizing the "max-min" condition number of the coefficient matrix within the context of linear error estimation. The proposed method is showcased on "virtual experiments" conducted via simulations to determine the optimal combinations of rotation angles along two coordinate axes. The method's feasibility is substantiated through a comparison with the distributions of parameter uncertainty. The results suggest that the proposed method holds promise as an alternative approach for comprehensive evaluation in the context of the MCO problem in XCD and various measurement scenarios.
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.
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.
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