Simulation and fabrication of slow light suspended air-bridge AlGaAs photonic crystal waveguide
Bibliographic record
Abstract
Photonic crystals can exhibit interesting optical properties such as peculiar dispersion, small group velocity, negative refraction and diffraction. Group velocity engineering in a certain wavelength range allows for enhanced nonlinear optical interactions, while higher light confinement achievable in photonic crystal waveguides leads to a reduced footprint for integrated photonic components. Silicon-based photonic crystals are relatively well studied,<sup>1–3</sup> however, they are not suitable for a monolithic integration with active devices. <sup>III-V</sup> semiconductors exhibit light-emitting properties, large Kerr nonlinearity and negligible two-photon absorption in the telecommunication regime, and therefore are more suitable for frequency conversion, all-optical signal processing, laser absorption spectroscopy and microcomb generation for correlation spectroscopy applications. In this work, we theoretically investigate the design and fabrication of photonic crystal waveguides based on an airbridge Al<sub>0.18</sub>Ga<sub>0.82</sub> slab for frequency conversion using FWM. We demonstrate a suspended Al0.18Ga0.82 layer fabricated by HF-controlled wet etching of an AlGaAs heterostructure, which selectively etched top and bottom claddings of higher aluminum concentration AlGaAs leaving behind the core suspended in air. We show, by simulations, that the group-index values of 25 over a bandwidth of 22 nm around 1590 nm in a dispersion-engineered W1 photonic crystal defect waveguide are possible enabling this device to operate in the slow-light regime where we also demonstrate a phase mismatch of nearly zero. Future designs can be optimized for longer wavelengths in the mid-infrared (MIR) as a promising platform to realize compact, slow-light enhanced integrated photonic components for sensing and wavelength conversion, thanks to the low propagation loss of AlGaAs in the MIR regime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".