Determination of Reactive RF-Sputtering Parameters for Fabrication of SiOx Films With Specified Refractive Index, for Highly Reflective SiOx Distributed Bragg Reflector
Bibliographic record
Abstract
Fabricating materials with specific refractive indices, which do not naturally exist in the nature, has always been an issue. This paper presents a method for fabricating SiOxfilms with specified refractive index. It is well known that the refractive index of reactively sputtered SiOxfilms depends on its deposition conditions; in this paper, this fact was employed to fabricate films with arbitrary refractive indices. A statistical study and a Genetic Algorithm are implemented that can determine the deposition conditions (including oxygen partial flow and pressure) for fabricating a film with an arbitrary refractive index in the range of 1.4-4.2. The method was experimentally shown to correctly determine the deposition conditions. The functionality of using the proposed method in fabricating optical components was further evaluated by fabricating a distributed Bragg reflector (DBR) consisting of 4.5 pairs, whose refractive indices of the layers were determined by the proposed method. The DBR featured a high 95% reflection in a bandwidth of more than 270 nm, which can be categorized as a high-quality DBR. The advance of the proposed method is that the films are made from a single target source without making any physical changes in the target or substrate positions.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".