@inproceedings{7aa5fe0e9b154a2d9838914915aa3504,
title = "Enhancing CD-SEM Accuracy with Attention-Boosted Noise2Noise Model",
abstract = "Semiconductor manufacturing relies on Critical Dimension Scanning Electron Microscopy (CD-SEM) for precision in resist pattern measurements. High-resolution CD-SEM images, while desirable, can damage the resist due to increased electron beam exposure with higher frame numbers. To address this, Noise2Noise, a deep-learning noise reduction method, is introduced. Noise2Noise employs multiple noise images for unsupervised noise reduction. However, it struggles with unknown samples and limited training data. This research enhances the Noise2Noise model by introducing Attention and Residual-Recurrent structures to extract high-precision images from low-resolution inputs (1 frame). The Attention-boosted Noise2Noise model in particular exhibits superior accuracy with improved Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) for unseen patterns. Overall, the modeling error characterized by (ΔCD/CD) has been reduced compared to the conventional Noise2Noise method, promising improved CD-SEM accuracy for advanced CMOS manufacturing.",
keywords = "CD-SEM, Unsupervised, accuracy, contour metrology, deep learning, denoise, neural network",
author = "Yu Okada and Hsuehli Liu and Lee, {Chieh En} and Tien, {Chung Hao} and Peichen Yu",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Metrology, Inspection, and Process Control XXXVIII 2024 ; Conference date: 26-02-2024 Through 29-02-2024",
year = "2024",
doi = "10.1117/12.3009959",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Sendelbach, {Matthew J.} and Schuch, {Nivea G.}",
booktitle = "Metrology, Inspection, and Process Control XXXVIII",
address = "美國",
}