| Autor: |
Ching-Hsuan Huang, Han-Chun Tung, Yen-Wei Feng, Hung-Tse Hsu, Hsueh-Li Liu, Albert Lin, Peichen Yu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 165979-165991 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3494816 |
| Popis: |
In semiconductor fabrication, any deviation leads to significant mistakes in the result. Thus, the proximity effect is a critical issue that must be solved. In the past, optical proximity correction was constructed by fabrication experience and physics formula models, resulting in difficulties when the technology node shrinks. As a result, optical proximity correction with machine learning models is highly expected to solve the issue in recent years. Due to the unique feature in optical proximity correction, single-flow convolutional feedback networks with customized attention layer are proposed to compete with widely used U-Net or U-Net with attention layer, which is the current mainstream in image-to-image machine learning tasks. The customized attention layer is used to replace the conventional attention layer. The proposed model with a customized attention layer has improved metrics compared to U-Net or U-Net with an attention layer. Compared the proposed model to U-Net with a cross-attention layer, we observe 3.74% improvement of modified mean pixel accuracy in the two-bar dataset, 0.9% improvement of modified mean pixel accuracy in the tri-bar dataset, 3.76% improvement of modified mean pixel accuracy in the polygon dataset and 2.06% improvement of modified mean pixel accuracy in the GAN400 dataset. The code is available at https://github.com/albertlin11/OPCfb. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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