Mechanical and thermoelastic characteristics of optical thin films deposited by dual ion beam sputtering
Bibliographic record
Abstract
Mechanical and thermoelastic properties of optical films are very important to ensure the performance of optical interference filters and optical coating systems. We systematically study the growth and the mechanical and thermoelastic characteristics of niobium oxide (Nb(2)O(5)), tantalum oxide (Ta(2)O(5)), and silicon dioxide (SiO(2)) thin films prepared by dual ion beam sputtering. First, we investigate the stress (sigma), hardness (H), reduced Young's modulus (E(r)), and scratch resistance. Second, we focus on the methodology and assessment of the coefficient of thermal expansion (CTE) and Poisson's ratio (nu) using the two-substrate method. For the high refractive index films, namely, Nb(2)O(5) (n at 550 nm=2.30) and Ta(2)O(5) (n at 550 nm=2.13), we obtained H approximately 6 GPa, E(r) approximately 125 GPa, CTE=4.9x10(-6) degrees C(-1), nu=0.22, and H approximately 7 GPa, E(r) approximately 133 GPa, CTE=4.4x10(-6) degrees C(-1), and nu=0.27, respectively. In comparison, for SiO(2) (n at 550 nm=1.48), these values are H approximately 9.5 GPa, E(r) approximately 87 GPa, CTE=2.1x10(-6) degrees C(-1), and nu=0.11. Correlations between the growth conditions (secondary beam ion energy and ion current), the microstructure, and the film properties are discussed.
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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".