Imaging with a Commercial Electron Backscatter Diffraction (EBSD) Camera in a Scanning Electron Microscope: A Review
Bibliographic record
Abstract
Scanning electron microscopy is widespread in field of material science and research, especially because of its high surface sensitivity due to the increased interactions of electrons with the target material’s atoms compared to X-ray-oriented methods. Among the available techniques in scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) is used to gather information regarding the crystallinity and the chemistry of crystalline and amorphous regions of a specimen. When post-processing the diffraction patterns or the image captured by the EBSD detector screen which was obtained in this manner, specific imaging contrasts are generated and can be used to understand some of the mechanisms involved in several imaging modes. In this manuscript, we reviewed the benefits of this procedure regarding topographic, compositional, diffraction, and magnetic domain contrasts. This work shows preliminary and encouraging results regarding the non-conventional use of the EBSD detector. The method is becoming viable with the advent of new EBSD camera technologies, allowing acquisition speed close to imaging rates. This method, named dark-field electron backscatter diffraction imaging, is described in detail, and several application examples are given in reflection as well as in transmission modes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".