Abstract
Image stitching is a technique that can significantly enlarge the scan area of scanning probe microscope (SPM) images. It is also the most commonly used method to cover large areas in high-speed SPM. In this paper, we provide details on stitching algorithms developed specifically to mitigate the effects of SPM error sources, namely the presence of scanner non-flatness. Using both synthetic data and flat samples we analyse the potential uncertainty contributions related to stitching, showing that the drift and line mismatch are the dominant sources of uncertainty. We also present the ‘flatten base’ algorithm that can significantly improve the stitched data results, at the cost of losing the large area form information about the sample.
| Original language | English |
|---|---|
| Article number | 125026 |
| Journal | Measurement Science and Technology |
| Volume | 35 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Dec 2024 |
| MoE publication type | A1 Journal article-refereed |
Funding
The work was supported by the 20IND08 MetExSPM project that has received funding from the EMPIR programme co-financed by the Participating States and from the European Union\u2019s Horizon 2020 research and innovation programme. This work was additionally partly funded by the UK Government\u2019s Department for Science, Innovation & Technology through the UK\u2019s National Measurement System programmes. The authors would like to thank PTB for manufacturing and providing the MetExSPM sample.
Keywords
- data processing
- SPM
- stitching
- uncertainty