EUV multilayer defect characterization via cycle-consistent learning

Chen, Ying; Lin, Yibo; Chen, Rui; Dong, Lisong; Wu, Ruixuan; Gai, Tianyang; Ma, Le; Su, Yajuan; Wei, Yayi

doi:10.1364/oe.394590

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Cited by 3 publications

References 11 publications

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EUV photomask defect detection based on image segmentation

Peng,

Xu,

Zhao

2023

Third International Conference on Optics and Image Processing (ICOIP 2023)

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EUV photomask defect detection based on image segmentation

Peng,

Xu,

Zhao

2023

Third International Conference on Optics and Image Processing (ICOIP 2023)

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Extreme ultraviolet phase defect characterization based on complex amplitudes of the aerial images

Cheng

Wang

et al. 2021

Appl. Opt.

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The profile deformation of a phase defect in an extreme ultraviolet (EUV) mask blank is the key factor to simulate its optical effects accurately and to compensate for it precisely. This paper provides a new, to the best of our knowledge, profile characterization method based on complex amplitudes of the aerial images for phase defects in EUV mask blanks. Fourier ptychography is adopted to retrieve the complex amplitudes of the aerial images and improve the lateral resolution. Both amplitude and phase impacted by the defect are taken into consideration to reconstruct the defect profile parameters (the height and the full width at half maxima of the defect’s top and bottom profiles). A conformal convolutional neural network model is constructed to map the amplitudes and phases of aerial images to the defect profile parameters. The Gaussian-shaped defect models with the mapped profile parameters can be used to simulate the amplitude and phase properties of the defects when compensating for them. The proposed method is verified to reconstruct the defect profile parameters of both bump defects and pit defects accurately. The involvement of both the amplitude and phase information makes the reconstructed defect profile parameters more appropriate to simulate the optical effects of the defects.

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Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography

Zheng

Cheng

et al. 2023

Appl. Opt.

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The multilayer defects of mask blanks in extreme ultraviolet (EUV) lithography may cause severe reflectivity deformation and phase shift. The profile information of a multilayer defect is the key factor for mask defect compensation or repair. This paper introduces an artificial neural network framework to reconstruct the profile parameters of multilayer defects in the EUV mask blanks. With the aerial images of the defective mask blanks obtained at different illumination angles and a series of generative adversarial networks, the method enables a way of multilayer defect characterization with high accuracy.

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EUV multilayer defect characterization via cycle-consistent learning

Cited by 3 publications

References 11 publications

EUV photomask defect detection based on image segmentation

EUV photomask defect detection based on image segmentation

Extreme ultraviolet phase defect characterization based on complex amplitudes of the aerial images

Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography

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