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 HfO<sub>2</sub> and SiO<sub>2</sub> and HfO<sub>2</sub> and Al<sub>2</sub>O<sub>3</sub> as the high index layer in the 2-layer coating structure. For comparison, HfO<sub>2</sub>/SiO<sub>2</sub> 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 HfO<sub>2</sub> is less than approximately 20%. The Hf<sub>0.2</sub>Si<sub>0.8</sub>O<sub>x</sub> AR coating 1-on-1 LIDT, 6.1 J/cm<sup>2</sup>, is similar to that measured in AR containing Al<sub>2</sub>O<sub>3</sub> as high index layer, 6.9 J/cm<sup>2</sup>. 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=10<sup>4</sup>. This fatigue behavior is also observed in the reference EBE HfO2/SiO2 AR sample. Instead, the IBS reference HfO<sub>2</sub>/SiO<sub>2</sub> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".