Facet type determination based on combined atomic force microscopy and electron backscatter diffraction
Bibliographic record
Abstract
The distribution of facet types affects the functionality of the surfaces of polycrystalline films. However, we are not aware of a previously published convenient method to determine their distribution. This work describes and demonstrates a process to determine and map the Miller indexes (hkl) of crystal facets exposed at the surfaces of polycrystalline films. To find facet types in non-trivial cases, one must know the orientation of the crystal and the direction in which the facet is facing. The method presented here combines the crystal orientations obtained with electron backscatter diffraction with the topography of the same sample area measured with atomic force microscopy. A challenging step is to transfer the data from the two instruments into a common coordinate system. The sequence of steps in the data processing is presented, with methods to verify the results. The process is illustrated with the analysis of an etched copper clad laminate (CCL) and an electroless Cu film deposited on the CCL. This example relates to facet selection in electroless and galvanic plating processes in printed circuit board production, where an uncontrolled transition from epitaxial to non-epitaxial growth can lead to surfaces with unacceptable roughness.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".