@inproceedings{a3b5ca13cf93462fa711151a8c1fe0c0,
title = "An Efficient Transformer-Based Approach for DUV Lithography SEM Image Denoising",
abstract = "Traditional Scanning Electron Microscopy (SEM) image noise reduction techniques, such as frame averaging or utilizing higher resolution SEM images, may result in potential electron beam damage and could also limit the speed of screening. In this paper, we propose a deep-learning-based denoising method using a Transformer-based architecture that addresses these challenges. This method effectively reduces noise while preserving image details, providing comparable measurements such as line width roughness that are only attainable with higher signal-to-noise ratio SEM images.",
keywords = "Lithography, SEM Image Denoising, Transformer, deep learning",
author = "Jiwei Shen and Botong Zhao and Hu Lu and Pengjie Lou and Wenzhan Zhou and Kan Zhou and Xintong Zhao and Shujing Lyu and Yue Lu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th International Workshop on Advanced Patterning Solutions, IWAPS 2023 ; Conference date: 26-10-2023 Through 27-10-2023",
year = "2023",
doi = "10.1109/IWAPS60466.2023.10366153",
language = "英语",
series = "IWAPS 2023 - 2023 7th International Workshop on Advanced Patterning Solutions",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Yayi Wei and Tianchun Ye",
booktitle = "IWAPS 2023 - 2023 7th International Workshop on Advanced Patterning Solutions",
address = "美国",
}