We configured a spectroscopic ellipsometer (SE) optical system with a micrometer spot size using reflective objectives. Two reflective objectives, based on the Schwarzchild configuration, are positioned just before and after the beam reflects on the sample. While we achieved the appropriate angle of incidence and numerical aperture (NA) of the light beam on the sample by utilizing an off-axis reflection configuration for the objectives, the polarization state of light is significantly influenced due to its asymmetric beam path, beam focusing, and reflection on the mirror surfaces. Therefore, we developed a proper model to describe the polarization state change arising from the effect of reflective objectives for our custom Spectroscopic Ellipsometer optical system and demonstrated its applicability for thin film structures.
We investigate on how to improve the performance of thickness determination from the optical scatterometry spectrum using machine learning. Our investigation is focused on a specific application for thick layered structures for 3D NAND with oxide/nitride repeating pairs. Since fast determination of thickness of every layer or detection of any outlier is not very efficient with the regression analysis using an optical model-based calculation due to requirement of a huge amount of calculations along with many parameters, machine learning (ML) can be applied for this application pursuing a faster solution. However, we also need to achieve its precision and accuracy as good as possible under a limited amount of ML train data sets. In order to carry out an efficient extraction or selection of features from data which is very important for improved performance of ML, we applied Fourier analysis of spectrum and investigate on how ML performance is improved.
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