In Situ Fabrication of Far-Detuned Mid-Infrared Optical Fiber Wavelength Converters
Bibliographic record
Abstract
Chalcogenide (ChG) glasses have been identified as promising materials for applications in nonlinear photonics due to their exceptionally high nonlinear refractive index; nearly three orders of magnitude beyond that of silica glass. A ChG fiber or waveguide that is tapered down into a microwire provides strong light confinement and enhanced nonlinear optical effect, it allows engineerable chromatic dispersion and thereby an easy access to nonlinear parametric processes such as four-wave mixing (FWM). In well-controlled conditions of chromatic dispersion ChG microwires may lead to strong parametric gain that is far-detuned with respect to the pump wavelength, by tens of THz [1,2], In theory, chromatic dispersion in a waveguide is precisely adjusted from a good control of waveguide geometry. In practice however, the amount of precision required in waveguide geometry as well as errors on the evaluation of refractive indices prevent the fabrication of wavelength converters with a predetermined wavelength offset. To illustrate this, fig. 1(a) shows the theoretical spectrum of a far-detuned ChG wavelength converter. A change in the core diameter by 5 mn results in a ~ 40 mn wavelength shift of the parametric sidebands. As an element of solution inspired from past reports, it has been shown with supercontinuum generation that experimental parameters could be finely tuned using an in situ monitoring technique [3], In this work, we demonstrate that wavelength conversion of a ChG microwire is precisely attained by in situ tracking of the microwire output during the tapering process, enabling the fabrication of wavelength converters that are precisely far-detuned by at least 34.3 THz.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".