Application of deep learning algorithms for Lithographic mask characterization-Reference-Cited by-同舟云学术

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Application of deep learning algorithms for Lithographic mask characterization

Published:2018-05-28 Issue: Volume: Page:
ISSN:
Container-title:Computational Optics II
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Author:

Woldeamanual Dereje Shewaseged,Erdmann Andreas,Maier Andreas

Publisher

SPIE

Reference13 articles.

1. Classification and printability of EUV mask defects from SEM images;Cho,2017

2. Optical lithography: Technology, physical effects, and modeling. Lectures in lithography;Erdmann,2016

3. Inverse Image Modeling for Defect Detection and Optical System Characterization;Xu,2016

4. Mask for extreme ultraviolet lithography;Yan,2005

5. Deep learning (2016);Goodfellow,2016

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. EUV photomask defect detection based on image segmentation;Third International Conference on Optics and Image Processing (ICOIP 2023);2023-08-01

2. Prediction of line-and-space pattern after etching based on scanning electron microscope image of corresponding resist pattern using machine learning;Advances in Patterning Materials and Processes XXXIX;2022-05-25

3. Classification of lines, spaces, and edges of resist patterns in scanning electron microscopy images using unsupervised machine learning;Japanese Journal of Applied Physics;2022-05-01

4. Fast inverse lithography based on dual-channel model-driven deep learning;Optics Express;2020-06-24

5. Applications of machine learning at the limits of form-dependent scattering for defect metrology;Metrology, Inspection, and Process Control for Microlithography XXXIII;2019-03-26

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