Reactive HiPIMS deposition of SiO2/Ta2O5 optical interference filters
Bibliographic record
Abstract
In this contribution, based on the detailed understanding of the processes at the target during reactive high power impulse magnetron sputtering (HiPIMS), we demonstrate the deposition of both low- and high-index films and their implementation in optical interference filters with enhanced performance. We first investigate strategies for stabilizing the arc-free HiPIMS discharges above Si and Ta targets in the presence of oxygen. We show that hysteresis can be suppressed for these two target materials by suitable pulse-management strategies, ensuring good process stability without having to rely on any feedback control. Afterwards, we discuss the room temperature deposition of optically transparent SiO2 and Ta2O5 single layers as well as the fabrication of SiO2/Ta2O5 stacks such as 7 layer Bragg reflectors and 11 layer Fabry-Perot interference filters. We also analyze the optical and mechanical characteristics of these various coatings and compare them with their counterparts obtained by radio-frequency magnetron sputtering (RFMS). Among other findings, we observe that the coatings prepared by HiPIMS present higher refractive index and lower surface roughness values, suggesting a denser microstructure. In addition, the HiPIMS-deposited optical filters exhibit a better optical performance than their counterparts fabricated by RFMS, but it is especially with respect to the mechanical properties such as scratch resistance and low residual stress, that the coatings prepared by HiPIMS present the most dramatic improvements (up to 42% and 72% enhancement, respectively). Finally, we show that the stress values obtained for the HiPIMS-deposited SiO2 and Ta2O5 coatings are lower than for other deposition techniques commonly used in the fabrication of optical interference filters.
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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".