Enhanced Control of Morphology in Thin Film Nanostructure Arrays
Bibliographic record
Abstract
Glancing angle deposition (GLAD) was used to grow thin films of silicon and titanium dioxide slanted post nanostructures onto periodically patterned substrates. The patterned substrates consisted of tetragonal arrays of small hillocks with periodicities of 100, 200, and 300 nm. An advanced substrate rotation algorithm called PhiSweep was used during the deposition. The PhiSweep algorithm consists of rotating the substrate back and forth such that the arriving vapour flux direction alternates from either side of desired column tilt direction. This reduces the anisotropy of the shadowing conditions, which diminishes column fanning. The tilt angle of the columns is affected by the PhiSweep parameters, which is important in applications such as square spiral photonic crystals. This relation is derived and confirmed with tilt angle measurements of the slanted post films. The films grown using the PhiSweep method were compared with similar films grown using traditional GLAD. The PhiSweep technique produced films which conformed to the initial periodic pattern much better than the films grown with traditional GLAD, enabling the growth of nanostructure arrays with smaller periodicities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".