Frequency conversion for infrared generation in monolithic semiconductor waveguides
Bibliographic record
Abstract
Widely tunable mid infrared radiation achievable using quantum cascade lasers (QCLs) often requires external cavities and several QCL chips to cover a large bandwidth similar to the range reported here (~ 1000s nm). The cost and mechanical stability of these designs leaves room for alternative more rugged approaches, which require no cavities to achieve very broad band tunability. While difference frequency generation (DFG) will unlikely match the power levels achievable from QCLs, it can provide spectral brightness and extremely wide tunablity, which can be valuable for numerous applications. Recently, we have demonstrated that dispersion engineering techniques can be used for phase matching of second order nonlinearities near the bandgap in monolithic waveguides. In this work we demonstrate an extremely simple structure to grow and fabricate, which utilizes dispersion engineering not only to achieve phase matching but also to expand the tuning range of the frequency conversion achieved in a waveguide through difference frequency generation. Frequency conversion in monolithic AlGaAs single-sided Bragg reflection waveguides using χ(2) nonlinearities produced widely tuneable, coherent infrared radiation between 2-3 μm and 7-9 μm. The broad tunability afforded by dispersion engineering and possible current injection, waveguide width chirping and temperature tuning makes it possible to produce a single multi-layer substrate to generate mid-IR signals that span μms in wavelength.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".