<i>In Situ</i> Focused Ion Beam Redeposition Surface Coatings for Site-Specific, Near-Surface Characterization by Atom Probe Tomography
Bibliographic record
Abstract
Atom probe tomography (APT) enables three-dimensional chemical mapping with near-atomic scale resolution. However, this method requires precise sample preparation, which is typically achieved using a focused ion beam (FIB) microscope. As the ion beam induces some degree of damage to the sample, it is necessary to apply a protective layer over the region of interest (ROI). Herein, the use of redeposition, a (frequently considered negative) side effect of FIB sputtering, is explored as a technique for targeted surface coatings in site-specific, near-surface APT investigations. In addition, the concept of "self-coating" is presented, which is the application of a capping layer using material from the same, or a similar, sample. It is shown to provide a pathway for high-quality coatings, as well as a method of minimizing the field evaporation threshold difference at the cap-sample interface, thus greatly reducing the likelihood of premature fractures. In situ redeposition surface coatings are shown to be versatile, with four materials used in the coating and analysis of two Si-based semiconductors and a Fe-Mn alloy. Several factors are discussed, such as the specimen yield, the capping layer quality, and the ease of ROI identification, all of which demonstrate its effectiveness in routine sample preparation workflows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".