Laser damage of UV hafnia-based multilayer dielectric coatings at 355 nm wavelength
Bibliographic record
Abstract
This work reports on the 1-on-1 and S-on-1 laser damage behavior of anti-reflection (AR) multilayer dielectric (MLD) coatings synthesized by biased target deposition (BTD) to include mixtures of HfO2 and SiO2 and HfO2 and Al2O3 as the high index layer in the 2-layer coating structure. For comparison, HfO2/SiO2 AR and HR coatings were also synthesized using ion beam sputtering (IBS) and ion beam assisted evaporation (EBE). The results show that in the BTD ARs the scaling of the 1-on-1 LIDT with the UV band-edge is not significant, unless the content of HfO2 is less than approximately 20%. The Hf0.2Si0.8Ox AR coating 1-on-1 LIDT, 6.1 J/cm2, is similar to that measured in AR containing Al2O3 as high index layer, 6.9 J/cm2. The S-on-1 LIDT of selected ARs shows a decrease of ~10% for S=10 and remains at the same level for up to S=104. This fatigue behavior is also observed in the reference EBE HfO2/SiO2 AR sample. Instead, the IBS reference HfO2/SiO2 HR coatings show the S-on-1 LIDT reduces with the increase in pulse number S. These results highlight the dominance of the materials’ properties and the substrate quality on affecting the laser damage behavior of AR coatings for λ=355 nm.
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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".