Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

• URL: http://arxiv.org/abs/2310.17997v1
• Date: Fri, 27 Oct 2023 09:16:56 GMT
• Title: Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
• Authors: Hui Sun, Hao Luo, Feifei Wang, Qingjiu Chen, Meng Chen, Xiaoduo Wang, Haibo Yu, Guanglie Zhang, Lianqing Liu, Jianping Wang, Dapeng Wu, Wen Jung Li
• Abstract summary: We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and scanning electron microscopy domain images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.
• Score: 16.152554659134246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum environment. We use deep learning to obtain the mapping relationship between optical super-resolution (OSR) images and SEM domain images, which enables the transformation of OSR images into SEM-like large depth-of-field images. Our custom-built scanning superlens microscopy (SSUM) system, which requires neither coating samples by conductive films nor a vacuum environment, is used to acquire the OSR images with features down to ~80 nm. The peak signal-to-noise ratio (PSNR) and structural similarity index measure values indicate that the deep learning method performs excellently in image-to-image translation, with a PSNR improvement of about 0.74 dB over the optical super-resolution images. The proposed method provides a high level of detail in the reconstructed results, indicating that it has broad applicability to chip-level defect detection, biological sample analysis, forensics, and various other fields.

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