Fourier analysis of spectrum for precision improvement in thickness determined by machine learning

Moon Il Shin; Inhee Joh; Sayyeon Joung; Jiwon Lee; Minhyeok Lee; Kyusik Kim; Shinyoung Ryu; Tae Dong Kang

doi:10.1117/12.2657792

27 April 2023 Fourier analysis of spectrum for precision improvement in thickness determined by machine learning

Moon Il Shin, Inhee Joh, Sayyeon Joung, Jiwon Lee, Minhyeok Lee, Kyusik Kim, Shinyoung Ryu, Tae Dong Kang

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 124962I (2023) https://doi.org/10.1117/12.2657792
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Moon Il Shin, Inhee Joh, Sayyeon Joung, Jiwon Lee, Minhyeok Lee, Kyusik Kim, Shinyoung Ryu, and Tae Dong Kang "Fourier analysis of spectrum for precision improvement in thickness determined by machine learning", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124962I (27 April 2023); https://doi.org/10.1117/12.2657792

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