A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons
Three-dimensional (3D) data analysis, especially 3D reconstruction, is critical within integrated circuits for quality control and inspection. The traditional 3D reconstruction, like multi-energy deconvolution scanning electron microscopy (MED-SEM), can achieved resolution of 5 nm in the horizontal...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11045923/ |
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| author | Caizhi Zheng Ronghan Hong Hao-Jie Hu Qing Huo Liu |
| author_facet | Caizhi Zheng Ronghan Hong Hao-Jie Hu Qing Huo Liu |
| author_sort | Caizhi Zheng |
| collection | DOAJ |
| description | Three-dimensional (3D) data analysis, especially 3D reconstruction, is critical within integrated circuits for quality control and inspection. The traditional 3D reconstruction, like multi-energy deconvolution scanning electron microscopy (MED-SEM), can achieved resolution of 5 nm in the horizontal direction and 10 nm in the vertical direction from backscattered electron images of objects at different depth layers. However, the traditional methods are difficult to obtain high-precision reconstruction results in the longitudinal direction, and the reconstruction results of most methods contain a large number of artifacts. Thus, in this work, a hybrid CNN-Transformer network, called BSE-VoxNets, is proposed for 3D high precision reconstruction. The proposed BSE-VoxNets consists three parts: transformer feature extractor, convolutional upsampling block and feature fusion block. 10000 BSE samples were used for training BSE-VoxNets, with 200 samples for testing, and the optimal cross-union ratio (IoU) of the test dataset reached 0.953. Two numerical cases are used to respectively verify the inversion performance of the proposed method for different depths and different feature scales. The reconstruction results show that the proposed BSE-VoxNets can reach a resolution of 2 nm in both the horizontal and vertical dimensions. |
| format | Article |
| id | doaj-art-4e8c995ef2b64253a4cd9109ee640e5b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e8c995ef2b64253a4cd9109ee640e5b2025-08-20T03:26:49ZengIEEEIEEE Access2169-35362025-01-011310855110856010.1109/ACCESS.2025.358197411045923A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered ElectronsCaizhi Zheng0https://orcid.org/0009-0003-3424-2171Ronghan Hong1Hao-Jie Hu2https://orcid.org/0009-0004-3189-1552Qing Huo Liu3https://orcid.org/0000-0001-5286-4423Institute of Electromagnetics and Acoustics, Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaTangshan Technology Company, Ningbo, ChinaInstitute of Electromagnetics and Acoustics, Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaInstitute of Electromagnetics and Acoustics, Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, ChinaThree-dimensional (3D) data analysis, especially 3D reconstruction, is critical within integrated circuits for quality control and inspection. The traditional 3D reconstruction, like multi-energy deconvolution scanning electron microscopy (MED-SEM), can achieved resolution of 5 nm in the horizontal direction and 10 nm in the vertical direction from backscattered electron images of objects at different depth layers. However, the traditional methods are difficult to obtain high-precision reconstruction results in the longitudinal direction, and the reconstruction results of most methods contain a large number of artifacts. Thus, in this work, a hybrid CNN-Transformer network, called BSE-VoxNets, is proposed for 3D high precision reconstruction. The proposed BSE-VoxNets consists three parts: transformer feature extractor, convolutional upsampling block and feature fusion block. 10000 BSE samples were used for training BSE-VoxNets, with 200 samples for testing, and the optimal cross-union ratio (IoU) of the test dataset reached 0.953. Two numerical cases are used to respectively verify the inversion performance of the proposed method for different depths and different feature scales. The reconstruction results show that the proposed BSE-VoxNets can reach a resolution of 2 nm in both the horizontal and vertical dimensions.https://ieeexplore.ieee.org/document/11045923/3D reconstructiondeep learningmachine learningscanning electron microscopy |
| spellingShingle | Caizhi Zheng Ronghan Hong Hao-Jie Hu Qing Huo Liu A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons IEEE Access 3D reconstruction deep learning machine learning scanning electron microscopy |
| title | A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons |
| title_full | A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons |
| title_fullStr | A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons |
| title_full_unstemmed | A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons |
| title_short | A Voxelized Transformer-Based Neural Network for 3D Reconstruction From Multi-Energy SEM Backscattered Electrons |
| title_sort | voxelized transformer based neural network for 3d reconstruction from multi energy sem backscattered electrons |
| topic | 3D reconstruction deep learning machine learning scanning electron microscopy |
| url | https://ieeexplore.ieee.org/document/11045923/ |
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