Group III-V semiconductors as promising nonlinear integrated photonic platforms
Bibliographic record
Abstract
Group III–V semiconductors are based on the elements of groups III and V of the periodic table. The possibility to grow thin-films made of binary, ternary, and quaternary III–V alloys with different fractions of their constituent elements allows for the precise engineering of their optical properties. In addition, since many III–V compounds are direct-bandgap semiconductors, they are suitable for the development of photonic devices and integrated circuits, especially when monolithic integration is required. Moreover, the strong optical nonlinearities of III–V materials enable a fertile field of research in photonic devices for all-optical signal processing, wavelength conversion, and frequency generation. Experimentally accessing the plethora of nonlinear optical phenomena in these materials considerably facilitates the exploration of light-matter interactions. Several demonstrations have explored the optical nonlinearities in waveguides, microring resonators, photonic crystal structures, quantum dots, and lasers. In this review, we survey numerous nonlinear optical studies performed in III–V semiconductor waveguide platforms. In particular, we discuss linear and nonlinear optical properties, material growth and fabrication processes, newer hybrid material platforms, and several nonlinear optical applications of III–V semiconductor integrated optical platforms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".