Work

Uncertainty quantification on small angle x-ray scattering measurement using Bayesian deep learning

Year: 2024

Type: article

Abstract: Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing.

Source: Journal of Applied Physics

DOI: 10.1063/5.0226735

Authors: Hairui Yang, Zhaolong Wu, Kezhong Zhang, Dawei Wang, Hong Yu

Institutions: Chinese Academy of Sciences, Shanghai Zhangjiang Laboratory, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences, Zhangjiang Laboratory

Language: English

Topic: X-ray Diffraction in Crystallography

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funder: National Natural Science Foundation of China