Super-Resolution Methods for Wafer Transmission Electron Microscopy Images

High-resolution wafer transmission electron microscopy (TEM) images have drawn considerable attention for measuring micro-patterns on semiconductor wafers. However, because wafer TEM images are nanoscale, acquiring high-resolution images entails a significant human effort. To minimize human intervention, deep learning-based super-resolution shows great potential for analyzing wafer TEM images. For wafer TEM images, it is crucial to learn the wafer TEM-specific noise stemming from scattered electron beams and instable magnetic fields. In addition, wafer TEM images can form pairs of low and high-resolution images by matching low and high-magnification images or either solely degrading high-resolution images. In this study, we examine four methods for constructing image pairs to effectively train super-resolution models tailored for wafer TEM images: (1) human labeling, (2) template matching, (3) bicubic degradation, and (4) complex degradation. In our experiments, image degradation-based complex degradation is the most suitable for wafer TEM images in terms of both super-resolution performance and cost. Furthermore, while image matching-based methods showed poor performance on typical noise, they effectively restored low-resolution images containing wafer TEM-specific noise. Such analyses can serve as comprehensive guidelines for constructing wafer TEM image super-resolution dataset.