Quantum well intermixing in InGaAsP laser structures using a low temperature grown InP cap layer
Bibliographic record
Abstract
Quantum well intermixing (QWI) in a 1.55 µm InGaAsP laser-like structure has been enhanced using the defects incorporated in an InP capping layer grown at low temperature (below the congruent sublimation temperature) by molecular beam epitaxy and subsequently subjected to rapid thermal annealing. The structures used had quantum wells (QWs) and barrier layers with identical group III compositions so the inter-diffusion occurs only on the group V sub-lattice. This inter-diffusion is induced by the diffusion of P-interstitials that result from the dissociation of P In anti-site defects that are present in large concentrations in the low temperature InP (LT-InP) layer. The magnitude of the QWI is determined by measuring the blueshift in the wavelength of room temperature photoluminescence emission from the QWs. It was found that the magnitude of the blueshift is dependent on the growth conditions of the LT-InP such that larger blueshifts are observed for LT-InP layers either grown at lower temperatures or with increasing P 2 overpressures. These features correlate with the expected changes in the concentration of P In defects with these changes in growth conditions. Also, there is a change in the rate of change in blueshift with the thickness of the LT-InP layer. For thin layers the rate of change of blueshift with thickness is rapid, but at a certain thickness a transition occurs to a lower rate of change with thickness. This transition thickness is temperature dependent such that the transition to the reduced rate occurs at larger thicknesses at higher anneal temperatures. This transition is interpreted as re-trapping of the P-interstitials in the LT-InP by the In-vacancies resulting from the P In dissociation which leads to a reduced rate of supply of P-interstitials into the underlying laser structure.
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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 | 0.000 |
| 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".