Advances in plasma-based atomic layer processing of AlF3 for the passivation of broadband aluminum mirrors
Bibliographic record
Abstract
Efficient and uniform Aluminum-based broadband mirrors are essential components for far-ultraviolet (FUV) astronomy. Plasma-enhanced atomic layer deposition (PEALD) is a low temperature, highly conformal coating process that has previously been demonstrated to produce high quality AlF3 films, although little has been reported on their performance in FUV applications. An ongoing collaboration between the US Naval Research Laboratory (NRL) and NASA Goddard Space Flight Center (GSFC) focuses on optimizing material properties of PEALD AlF3 coatings on Al mirrors to enhance FUV optical performance. PEALD AlF3 films were deposited using trimethylaluminum and SF6 plasma precursors in a modified Veeco Fiji G2 reactor. ALD growth windows (the range of process parameters resulting in ideal growth) were established using an in situ ellipsometer to monitor the fluoride growth rate directly on Al substrates and supplemented with post-deposition x-ray photoelectron spectroscopy to elucidate process-structure property relationships. Optimal AlF3 films had a growth rate of 0.75-0.8Å/cycle, F/Al ratio of ≈3, < 2 at% O, indicating that PEALD is a beneficial process technique towards achieving optical coatings on a variety of potential mirror materials. The influence of PEALD parameters on the FUV optical performance of Al mirrors overcoated with PEALD-AlF3 will be also discussed.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".