Thickness and density evaluation for nanostructured thin films by glancing angle deposition
Bibliographic record
Abstract
Thickness evaluation is a particular challenge encountered in the fabrication of nanosculptured thin films fabricated by glancing angle deposition (GLAD). In this article, we deduce equations which allow for accurate in situ thickness monitoring of GLAD thin films deposited onto substrates tilted with respect to the direction of incoming vapor. Universal equations are derived for the general case of Gaussian vapor flux distribution, off-axis sensors, variable substrate tilt, and nonunity sticking coefficient. The mathematical description leads to an incidence angle dependence of thickness and density, allowing for quantitative prediction of porosity in samples with different morphologies and thickness calibrations. In addition, variation of sticking probability with the incidence angle creates a nonmonotonic variation of the film thickness and porosity with the substrate tilt. We discuss the implications of the substrate type, sensor type, and source geometry in a precise quantitative determination of the thickness of thin films fabricated on tilted substrates. Our equations can be particularized for the case of films fabricated at normal incidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".