Automated stitching for scanning electron microscopy images of integrated circuits

Abstract

While established toolchains are widely available for reverse engineering software, firmware, and even hardware on the printed circuit board level to some degree, the entry barrier to reverse engineering of integrated circuits (ICs) remains high due to the associated cost of equipment. One key driver of this high cost is the requirement of high-quality scanning electron microscopes (SEMs) for the analysis of ICs with small feature sizes. While optical microscopes are a cost-effective alternative for large feature sizes, modern ICs are manufactured with feature sizes that are too small for the limited magnification of optical microscopes. 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 cheap, used SEMs. This thesis introduces and evaluates several algorithms for the purpose of fusing noisy images with low contrast created by older SEMs. Based on an evaluation using gigapixel scale image sets, the most efficient and effective image registration algorithm for these image properties is determined. These algorithms determine offsets between individual overlapping images in the image set. For the two best algorithms, automated detection of optimal parameters is developed. Four global stitching algorithms are introduced to create large fused images based on the results of image registration. These four algorithms optimize for different quality metrics in the generated fused image. Finally, the introduced algorithms are evaluated and compared to state-of-the-art image stitching software.