Mixup-based Neural Network for Image Restoration and Structure Prediction from SEM Images
Scanning electron microscopy (SEM) has been widely used for the semiconductor industry since it provides high-resolution details of the semiconductor. However, there is a gap in research for various tasks (i.e., image restoration and structure prediction) in SEM datasets collected under various cond...
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Published in | IEEE transactions on instrumentation and measurement Vol. 73; p. 1 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Scanning electron microscopy (SEM) has been widely used for the semiconductor industry since it provides high-resolution details of the semiconductor. However, there is a gap in research for various tasks (i.e., image restoration and structure prediction) in SEM datasets collected under various conditions. Therefore, we introduce a new SEM dataset with diverse characteristics such as energy, noise, current with various levels for image restoration and structure prediction. Furthermore, we propose a new deep learning-based method for this dataset. The method consists of two stages: image restoration stage and structure prediction stage. In the image restoration stage, we design the transformer-based architecture to utilize pixel information widely. In the structure prediction stage, we introduce a novel training algorithm, SEMixup, and a novel CNN-based network, SEM-SPNet. Specifically, SEMixup overcome the generalization and robustness of SEM-SPNet by implicitly interpolating a pair of samples and their labels. Experiments demonstrate that our method achieves state-of-the-art results across all dataset conditions. This work expands the possibilities of SEM image analysis using deep learning, contributing to the semiconductor industry. |
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ISSN: | 0018-9456 1557-9662 |
DOI: | 10.1109/TIM.2024.3366569 |