Bibliographic record
Abstract
Plasma enhanced chemical vapor deposition (PECVD) is being increasingly used for the fabrication of transparent dielectric optical films and coatings. This involves single-layer, multilayer, graded index, and nanocomposite optical thin film systems for applications such as optical filters, antireflective coatings, optical waveguides, and others. Beside their basic optical properties (refractive index, extinction coefficient, optical loss), these systems very frequently offer other desirable “functional” characteristics. These include hardness, scratch, abrasion, and erosion resistance, improved adhesion to various technologically important substrate materials such as polymers, hydrophobicity or hydrophilicity, long-term chemical, thermal, and environmental stability, gas and vapor impermeability, and others. In the present article, we critically review the advances in the development of plasma processes and plasma systems for the synthesis of thin film high and low index optical materials, and in the control of plasma–surface interactions leading to desired film microstructures. We particularly underline those specificities of PECVD, which distinguish it from other conventional techniques for producing optical films (mainly physical vapor deposition), such as fabrication of graded index (inhomogeneous) layers, control of interfaces, high deposition rate at low temperature, enhanced mechanical and other functional characteristics, and industrial scaleup. Advances in this field are illustrated by selected examples of PECVD of antireflective coatings, rugate filters, integrated optical devices, and others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".