Figure | Working Principle of the Demonstrated Method and the Thickness Characterization Results (IMAGE)
Caption
a, Working principle of the demonstrated method. The samples-under-test were multilayer semiconductor devices with alternating layers of oxide (SiO2) and nitride (Si3N4) on a silicon substrate. To obtain the spectroscopic data, commercial ellipsometers and reflectometers installed in the semiconductor production lines were used. For the machine learning model, measured spectral data and each layer thickness were used as input and output, respectively. b, Thickness prediction results for the 23 test samples. The predicted thickness (red circles) matches well with the actual thickness (blue triangles), regardless of the material or layer position, with an average prediction RMSE of approximately 1.6 Å. c, Outlier device detection results. Seventeen normal samples and two outlier samples were prepared for the test. All of the normal and outlier samples are successfully classified.
Credit
by Hyunsoo Kwak, Sungyoon Ryu, Suil Cho, Junmo Kim, Yusin Yang, and Jungwon Kim
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