All-fiber ellipsometer for nanoscale dielectric coatings
Bibliographic record
Abstract
Multiple mode resonance shifts in tilted fiber Bragg gratings (TFBGs) are used to simultaneously measure the thickness and the refractive index of TiO2 thin films formed by Atomic Layer Deposition (ALD) on optical fibers. This is achieved by comparing the experimental wavelength shifts of 8 TFBG resonances during the deposition process with simulated shifts from a range of thicknesses (T) and values of the real part of the refractive index (n). The minimization of an error function computed for each (n, T) pair then provides a solution for the thickness and refractive index of the deposited film and, a posteriori, to verify the deposition rate throughout the process from the time evolution of the wavelength shift data. Validations of the results were carried out with a conventional ellipsometer on flat witness samples deposited simultaneously with the fiber and with scanning electron measurements on cut pieces of the fiber itself. The final values obtained by the TFBG (n = 2.25, final thickness of 185 nm) were both within 4% of the validation measurements. This approach provides a method to measure the formation of nanoscale dielectric coatings on fibers in situ for applications that require precise thicknesses and refractive indices, such as the optical fiber sensor field. Furthermore, the TFBG can also be used as a process monitor for deposition on other substrates for deposition methods that produce uniform coatings on dissimilar shaped substrates, such as ALD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".