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Scanning electron microscopy (SEM) is indispensable in diverse applicationsranging from microelectronics to food processing because it provides largedepth-of-field images with a resolution beyond the optical diffraction limit.However, the technology requires coating conductive films on insulator samplesand a vacuum environment. We use deep learning to obtain the mappingrelationship between optical super-resolution (OSR) images and SEM domainimages, which enables the transformation of OSR images into SEM-like largedepth-of-field images. Our custom-built scanning superlens microscopy (SSUM)system, which requires neither coating samples by conductive films nor a vacuumenvironment, is used to acquire the OSR images with features down to ~80 nm.The peak signal-to-noise ratio (PSNR) and structural similarity index measurevalues indicate that the deep learning method performs excellently inimage-to-image translation, with a PSNR improvement of about 0.74 dB over theoptical super-resolution images. The proposed method provides a high level ofdetail in the reconstructed results, indicating that it has broad applicabilityto chip-level defect detection, biological sample analysis, forensics, andvarious other fields.

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