Laser damage of UV hafnia-based multilayer dielectric coatings at 355 nm wavelength
Bibliographic record
Abstract
Megajoule ultraviolet nanosecond pulses were used to demonstrate ignition at the National Ignition Facility in December 2022 [ Nature 601 , 542 ( 2022 ) 10.1038/s41586-021-04281-w ]. The energy scaling of laser fusion drivers relies, in part, on significant improvements in the UV multilayer dielectric (MLD) coatings, which are critical optics in the laser architecture. This paper reports on the laser damage behavior of MLD coatings based on HfO 2 , SiO 2 , and Al 2 O 3 , designed for operation at λ =355nm. Two-layer anti-reflection (AR) coatings containing HfO 2 -based mixtures were co-sputtered by reactive biased target deposition (BTD) as the high-index layer. The laser-induced damage threshold was assessed from 1-on-1 and S-on-1 tests and compared to MLD ARs of HfO 2 /SiO 2 fabricated by ion beam sputtering and electron beam evaporation. It is shown that the 1-on-1 LIDT of BTD ARs, which contain Hf 1−y Si y O x and Hf 1−y Al y O x mixtures, is higher than that of BTD HfO 2 /SiO 2 ARs. However, this increase is below that predicted by the scaling of LIDT with the optical bandgap, calculated from the LIDT of HfO 2 /SiO 2 and HfO 2 /Al 2 O 3 . The S-on-1 LIDT of BTD ARs decreases by ∼25% for S=10 and remains unchanged to S=10 4 laser shots, indicating no accumulation fatigue. Neither UV preconditioning nor etching of the substrate prior to coating deposition caused a major improvement in the 1-on-1 LIDT of BTD ARs.
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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".