Advanced Processing of Scanning Electron Microscopy Images in 2-D and 3-D Datasets

To acquire high-resolution Scanning Electron Microscopy (SEM) images over wide areas, we must acquire several images ``tiling'' the surface and assemble them into a single composite image, using a process called image stitching. While for some applications, stitching is now routine, SEM mosaics of semiconductors pose several challenges: (1) by design, the image features (wire, via and dielectric) are highly repetitive, (2) the overlap between image tiles is small, (3) sample charging causes intensity variation between captures of the same region, and (4) machine instability causes non-linear deformation within tiles and between tiles. In this study, we compare the accuracy and computational cost of three well-known pixel-based techniques: Fast Fourier Transform (FFT), Sum of Squared Differences (SSD), and Normalized Cross Correlation (NCC). We compare well-known 2D algorithms, as well as novel projection-onto-1D versions. The latter reduces the computational complexity from O(n^2) to O(n), where n is the number of pixels, without loss of accuracy, and in some cases, with greater accuracy. Another approach to reducing the computational complexity of image alignment is to compare isolated landmarks, rather than pixels. In semiconductor images, there are no natural fiducials and adding them would destroy the information required to reconstruct their circuits, so we introduce a new class of landmarks which we call numerical landmarks. Related to Harris corners, the novel numerical landmarks are insensitive to brightness variations and noise. Finally, we consider the alignment problem between layers of image mosaics. Unlike in the ``horizontal'' directions, the vertical dimension is only sparsely sampled. Consequently, image features and landmarks cannot be used for alignment. Instead, we must rely on the relationship between vias (through-plane metalization) and wires (in-plane metalization), and we have developed a novel algorithm for matching vias in the lower layer with wires above, and use this to align subimages.

Department dc:contributor.department

Computing and Software

Year dc:date.issued

2023

Author dc:creator

• Khoonkari, Nasim

Advisors dc:contributor.advisor

• Anand, Christopher
• Bassim, Nabil

• Scanning Electron Microscopy
• SEM
• 2D Stitching
• 3D Reconstruction
• Semiconductor
• Image Processing

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11375/28872

OAI record oai:identifier

https://macsphere.mcmaster.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Amacsphere.mcmaster.ca%3A11375%2F28872