Planar Quarter Wave Stack Reflectors Prepared from Chalcogenide Ge-Se and Polymer Polystyrene Thin Films
Bibliographic record
Abstract
Dielectric and Metal/dielectric Reflectors In this study, we have shown that dielectric and metal/dielectric reflectors with the optical reflectivity higher than r > 99 % can be fabricated using low-temperature and technologically simple deposition techniques from chalcogenide glasses and polymer films. We aimed rather at an investigation of the optical properties of the used materials and dielectric and metal/dielectric reflectors, than at the fabrication of reflectors with the highest practically achievable optical reflectivity, which would of course demand a much higher number of deposited layers. The factors determining the quality of Ge-Se/PS reflectors and dielectric reflectors in general are an inaccuracy in thin film deposition, homogeneity of film optical properties and their surface roughness. The inaccuracy in a film deposition was ± 7 nm for chalcogenide and ± 17 nm for polymer films according to ellipsometric results. It represents roughly a ~5 % deviation in thickness. In our opinion, this value can be further reduced maximally up to the level of 1-2% especially if we consider that the main issue remains the thickness homogeneity of spin-coated films across their entire surface. For comparison, the inaccuracy in thickness for crystalline films prepared using more sophisticated and more expensive vacuum deposition methods such as an e-beam or CVD is under 1% (Yao et al., 2007 and Perez et al., 2005). In case of 1D-photonic crystals, i.e., multilayers a film thickness inaccuracy in order of percent units has basically a low impact on the optical properties of such multilayers.
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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) | 0.002 | 0.001 |
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".