  We report resolution enhancement in scanning electron microscopy (SEM) images
using a generative adversarial network. We demonstrate the veracity of this
deep learning-based super-resolution technique by inferring unresolved features
in low-resolution SEM images and comparing them with the accurately
co-registered high-resolution SEM images of the same samples. Through spatial
frequency analysis, we also report that our method generates images with
frequency spectra matching higher resolution SEM images of the same
fields-of-view. By using this technique, higher resolution SEM images can be
taken faster, while also reducing both electron charging and damage to the
samples.
