Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes
Overlay accuracy is a fundamental indicator of a photolithography machine performance. Misalignment between the mask and wafer is the main factor affecting overlay accuracy. The photolithographic alignment method, which uses Moiré fringes, is notable for its straightforward optical path a...
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IEEE
2025-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10970258/ |
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| author | Dajie Yu Junbo Liu Chuan Jin Yuyang Li Kairui Zhang Ji Zhou |
| author_facet | Dajie Yu Junbo Liu Chuan Jin Yuyang Li Kairui Zhang Ji Zhou |
| author_sort | Dajie Yu |
| collection | DOAJ |
| description | Overlay accuracy is a fundamental indicator of a photolithography machine performance. Misalignment between the mask and wafer is the main factor affecting overlay accuracy. The photolithographic alignment method, which uses Moiré fringes, is notable for its straightforward optical path and high precision. However, the alignment accuracy is significantly influenced by the Moiré fringe phase analysis algorithm. This paper proposes a hybrid deep learning architecture based on a Vision Transformer for Moiré fringe phase analysis. By training on various types of Moiré fringe datasets, the model can predict the fringe wrapping phase, allowing for the analysis of elements within the wrapping phase, including displacement information. This method combines the multi-head attention mechanism of a Vision Transformer with deep learning feature extraction to build a hybrid deep learning architecture. This model effectively learns the mathematical mapping between the Moiré fringe phase information and actual offset, accurately outputting true Moiré fringe phase data. Results show that despite the presence of Gaussian noise and tilted states, the hybrid architecture maintains a Root Mean Square Error (RMSE) within the range of 6–7 nm, and a Structural Similarity Index (SSIM) above 0.70, and Peak Signal-to-Noise Ratio (PSNR) is consistently maintained above 36. Consequently, the proposed model demonstrates superior robustness in handling noisy data compared to existing phase retrieval techniques. Additionally, the model has been optimized in structure to more efficiently extract phase information from complex Moiré fringe patterns. This study offers valuable insights for expanding Moiré fringe imaging applications. |
| format | Article |
| id | doaj-art-4e236a15028b4adaa4d0e202c33075e7 |
| institution | DOAJ |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-4e236a15028b4adaa4d0e202c33075e72025-08-20T03:07:14ZengIEEEIEEE Photonics Journal1943-06552025-01-011731810.1109/JPHOT.2025.356254210970258Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré FringesDajie Yu0https://orcid.org/0009-0002-3356-6847Junbo Liu1https://orcid.org/0000-0002-1447-7606Chuan Jin2Yuyang Li3Kairui Zhang4Ji Zhou5https://orcid.org/0000-0001-8028-0134National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaNational Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Science, Chengdu, Sichuan, ChinaOverlay accuracy is a fundamental indicator of a photolithography machine performance. Misalignment between the mask and wafer is the main factor affecting overlay accuracy. The photolithographic alignment method, which uses Moiré fringes, is notable for its straightforward optical path and high precision. However, the alignment accuracy is significantly influenced by the Moiré fringe phase analysis algorithm. This paper proposes a hybrid deep learning architecture based on a Vision Transformer for Moiré fringe phase analysis. By training on various types of Moiré fringe datasets, the model can predict the fringe wrapping phase, allowing for the analysis of elements within the wrapping phase, including displacement information. This method combines the multi-head attention mechanism of a Vision Transformer with deep learning feature extraction to build a hybrid deep learning architecture. This model effectively learns the mathematical mapping between the Moiré fringe phase information and actual offset, accurately outputting true Moiré fringe phase data. Results show that despite the presence of Gaussian noise and tilted states, the hybrid architecture maintains a Root Mean Square Error (RMSE) within the range of 6–7 nm, and a Structural Similarity Index (SSIM) above 0.70, and Peak Signal-to-Noise Ratio (PSNR) is consistently maintained above 36. Consequently, the proposed model demonstrates superior robustness in handling noisy data compared to existing phase retrieval techniques. Additionally, the model has been optimized in structure to more efficiently extract phase information from complex Moiré fringe patterns. This study offers valuable insights for expanding Moiré fringe imaging applications.https://ieeexplore.ieee.org/document/10970258/Deep learninghybrid architecturephase analysisMoiré fringes |
| spellingShingle | Dajie Yu Junbo Liu Chuan Jin Yuyang Li Kairui Zhang Ji Zhou Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes IEEE Photonics Journal Deep learning hybrid architecture phase analysis Moiré fringes |
| title | Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes |
| title_full | Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes |
| title_fullStr | Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes |
| title_full_unstemmed | Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes |
| title_short | Deep Learning Hybrid Architecture Based on Vision Transformer for Phase Analysis of Moiré Fringes |
| title_sort | deep learning hybrid architecture based on vision transformer for phase analysis of moir x00e9 fringes |
| topic | Deep learning hybrid architecture phase analysis Moiré fringes |
| url | https://ieeexplore.ieee.org/document/10970258/ |
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