Effect of the substrate temperature and deposition rate on the initial growth of thin lithium niobate-tantalate films deposited from a thermal plasma
Bibliographic record
Abstract
Thin lithium niobate-tantalate (LiNb 0.5 Ta 0.5 O 3 ) films are studied at the initial stage of deposition from a thermal plasma. The effect of two deposition parameters (the substrate temperature and the deposition rate) on the film morphology, the film crystallinity, and the density of nuclei growing on a (0001) sapphire substrate are investigated. It is shown that the crystalline structure and roughness of a film are determined, for the most part, in the initial growth stage and therefore depend directly on both parameters. At the optimum temperatures and growth rates for obtaining good characteristics of (0006) texture, crystallinity, and surface roughness of the films, the film nuclei on the substrate have a high density and good epitaxial orientation to it. If the growth conditions are not optimum, the islands are either amorphous or have a low density on the substrate surface. The nucleation activation energy is observed to decrease as the deposition rate increases, which supports the assumption that the species that are active in film deposition are “hot” clusters forming in an oxygen-argon plasma in the immediate vicinity of the substrate.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".