An Efficient Transformer-Based Approach for DUV Lithography SEM Image Denoising
Jiwei Shen, Botong Zhao, Hu Lu, Pengjie Lou, Wenzhan Zhou, Kan Zhou, Xintong Zhao, Shujing Lyu, Yue Lu · 2023
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.