Automated Stitching of Noisy Scanning Electron Microscopy Images for Integrated Circuit Reverse Engineering
Daniel Burian, Christian Kudera, Michael Pucher, Georg Merzdovnik · 2022
A scanning electron microscope (SEM) usually creates images in the range of megapixel resolutions, but analyzing an IC layer requires resolutions in the gigapixel range. To create such large images, many individual images must be taken and then fused into one large image, which poses unique challenges: SEM images are affected by distortion due to charging effects and often exhibit high levels of noise and low contrast. One way of reducing the entry barrier to IC reverse engineering is to develop algorithms that can provide good results even in the case of suboptimal image quality, as can be produced by comparatively older, pre-owned SEMs. The main contribution of this work is the introduction and evaluation of four new algorithms, capable of composing high noise and low contrast SEM images into fused images. While the problem of stitching small images into one fused image is not new, the application of stitching algorithms for noisy IC images poses challenges that have not been addressed in the literature.