  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.
