SEM image contour extraction with deep learning method

Junhao Gu; Yingying Shang; Peng Xu; Juan Wei; Song Sun; Qingchen Cao; Jiangliu Shi; Xijin Zhao; Chun Zhang

doi:10.1117/12.2686135

21 November 2023 SEM image contour extraction with deep learning method

Junhao Gu, Yingying Shang, Peng Xu, Juan Wei, Song Sun, Qingchen Cao, Jiangliu Shi, Xijin Zhao, Chun Zhang

Proceedings Volume 12751, Photomask Technology 2023; 1275114 (2023) https://doi.org/10.1117/12.2686135
Event: SPIE Photomask Technology + EUV Lithography, 2023, Monterey, California, United States

Conference Poster

Abstract

The contour data extracted from SEM wafer images after the lithography are widely used in the critical dimension (CD), edge placement error (EPE) measurement. It is important to obtain the contours fast and accurate before the analysis of lithographic process and calibration of the lithographic models. Without the accurate contour data, the complete CDU, PVband analysis and inverse lithography technique are hard to realize. With the continuous shrink of the technology nodes, the demand for the accurate contour extraction increases. However, fast and accurate contour extraction from SEM images with defects and noises is challenging. We apply the U-Net to the semantic segmentation of SEM images. The contour extraction and evaluation can be done better after the image segmentation. Our experimental results show that satisfactory contour data of various types of lithographic patterns can be obtained with noisy SEM images.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhao Gu, Yingying Shang, Peng Xu, Juan Wei, Song Sun, Qingchen Cao, Jiangliu Shi, Xijin Zhao, and Chun Zhang "SEM image contour extraction with deep learning method", Proc. SPIE 12751, Photomask Technology 2023, 1275114 (21 November 2023); https://doi.org/10.1117/12.2686135

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