Capability for Fine Tuning of the Refractive Index Sensing Properties of Long-Period Gratings by Atomic Layer Deposited Al2O3 Overlays
Bibliographic record
Abstract
This work presents an application of thin aluminum oxide (Al2O3) films obtained using atomic layer deposition (ALD) for fine tuning the spectral response and refractive-index (RI) sensitivity of long-period gratings (LPGs) induced in optical fibers. The technique allows for an efficient and well controlled deposition at monolayer level (resolution ~ 0.12 nm) of excellent quality nano-films as required for optical sensors. The effect of Al2O3 deposition on the spectral properties of the LPGs is demonstrated experimentally and numerically. We correlated both the increase in Al2O3 thickness and changes in optical properties of the film with the shift of the LPG resonance wavelength and proved that similar films are deposited on fibers and oxidized silicon reference samples in the same process run. Since the thin overlay effectively changes the distribution of the cladding modes and thus also tunes the device’s RI sensitivity, the tuning can be simply realized by varying number of cycles, which is proportional to thickness of the high-refractive-index (n > 1.6 in infrared spectral range) Al2O3 film. The advantage of this approach is the precision in determining the film properties resulting in RI sensitivity of the LPGs. To the best of our knowledge, this is the first time that an ultra-precise method for overlay deposition has been applied on LPGs for RI tuning purposes and the results have been compared with numerical simulations based on LP mode approximation.
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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) | 0.000 | 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".