In-situ observation of nucleation and property evolution in films grown with an atmospheric pressure spatial atomic layer deposition system
Bibliographic record
Abstract
Abstract Atmospheric pressure—spatial atomic layer deposition (AP-SALD) is a promising open-air deposition technique for high-throughput manufacturing of nanoscale films, yet the nucleation and property evolution in these films has not been studied in detail. In this work, in situ reflectance spectroscopy was implemented in an AP-SALD system to measure the properties of Zinc oxide (ZnO) and Aluminum oxide (Al2O3) films during their deposition. For the first time, this revealed a substrate nucleation period for this technique, where the length of the nucleation time was sensitive to the deposition parameters. The in situ characterization of thickness showed that varying the deposition parameters can achieve a wide range of growth rates (0.1–3 nm/cycle), and the evolution of optical properties throughout film growth was observed. For ZnO, the initial bandgap increased when deposited at lower temperatures and subsequently decreased as the film thickness increased. Similarly, for Al2O3 the refractive index was lower for films deposited at a lower temperature and subsequently increased as the film thickness increased. Notably, where other implementations of reflectance spectroscopy require previous knowledge of the film’s optical properties to fit the spectra to optical dispersion models, the approach developed here utilizes a large range of initial guesses that are inputted into a Levenberg-Marquardt fitting algorithm in parallel to accurately determine both the film thickness and complex refractive index.
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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".