Geometry-dependent two-photon absorption followed by free-carrier absorption in AlGaAs waveguides
Bibliographic record
Abstract
Nonlinear absorption can limit the efficiency of nonlinear optical devices. However, it can also be exploited for optical limiting or switching applications. Thus, characterization of nonlinear absorption in photonic devices is imperative for designing useful devices. This work uses the nonlinear transmittance technique to measure the two-photon absorption coefficients ( α 2 ) of AlGaAs waveguides in strip-loaded, nanowire, and half-core geometries in the wavelength range from 1480 to 1560 nm. The highest α 2 values of 2.4, 2.3, and 1.1 c m / G W were measured at 1480 nm for 0.8-µm-wide half-core, 0.6-µm-wide nanowire, and 0.9-µm-wide strip-loaded waveguides, respectively, with α 2 decreasing with increasing wavelength. The free-carrier absorption cross section was also estimated from the nonlinear transmittance data to be around 2.2 × 10 − 16 c m 2 for all three geometries. Our results contribute to a better understanding of the nonlinear absorption in heterostructure waveguides of different cross-sectional geometries. We discuss how the electric field distribution in the different layers of a heterostructure can lead to geometry-dependent effective two-photon absorption coefficients. More specifically, we pinpoint the third-order nonlinear confinement factor as a design parameter to estimate the strength of the effective nonlinear absorption, in addition to tailoring the bandgap energy by varying the material composition.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".