Characterization of fiber Bragg gratings written using a remote writing technique and a coherent CW UV source
Bibliographic record
Abstract
Traditionally in the process of writing fiber Bragg gratings with a phase mask the fiber is placed near or in close contact with the mask. With low coherence excimer sources this is necessary because the fringe visibility is greatly reduced beyond 500 μm. As a result of these limitations there has been increased interest in understanding the interference phenomena associated with a phase mask. During the past year we studied the beam interference phenomena associated with ultrafast gratings. We observed that with these coherent sources it was possible to write gratings remotely (Phase Mask-Fiber distance of ≈1 cm). In addition to this we observed evidence of walk-off between mask orders that significantly affected the interference patterns. In this paper we demonstrate that a frequency doubled Argon-ion laser, being a coherent source, can be used to inscribe fiber Bragg gratings at large distances from the phase mask (> 15 mm). We will demonstrate that walk-off between mask orders will change the interference profile along the grating length. We show that the spectral profile correlates with the calculated interference pattern. Beam walk-off effects play a role in the inscription of any photonic device with a phase mask. This remote writing technique can be used to tailor the index modulation pattern in the fiber and could potentially be used to produce two beam interference gratings even in the presence of a significant zero order amplitude.
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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 | 0.001 |
| 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".