Material properties of ion beam deposited oxides for the optoelectronic industry
Bibliographic record
Abstract
High quality, dense films of SiO2, Al2O3, Ta2O5, and TiO2 were deposited with an ion beam deposition system (IBD). IBD has significant advantages over other techniques in terms of directionality, stress control, repeatability, thermal stability, and film uniformity [J. J. Cuomo, J. M. E. Harper, C. R. Guarnieri, D. S. Yee, L. J. Atanasio, J. Angilello, C. T. Wu, and R. H. Hammond, J. Vac. Sci. Technol. 20, 349 (1982)]. To decrease the surface damage induced by ion bombardment, a multi-energy process was developed. This is especially important for laser facet coatings. The oxide films were optimized for the desired refractive index and zero absorption. Stress values of −0.2 to −0.5 GPa (compressive) and extremely good uniformity (<0.2%) of film thickness and refractive index were obtained. The impact of the IBD deposition angle on film stress was also investigated. These films showed excellent thermal stability with virtually no degradation of optical properties after three 1 h anneals at 350 °C (Δn<0.0124). With the exception of TiO2, all films remained in compressive stress after annealing. TiO2 turned tensile. With the variety of oxide materials developed, designing an anti-reflective or highly reflective stack, which satisfies requirements of stress, uniformity, deposition rate, and reflectance, becomes a matter of choosing the appropriate material set.
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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".