The effect of different proximity caps on quantum well intermixing in InGaAsP/InP QW structures
Bibliographic record
Abstract
A low-temperature InP-cap layer is used to enhance quantum well intermixing (QWI) following rapid thermal annealing (RTA) on an InGaAsP quantum well (QW) structure. The influence of different proximity caps (Si, InP and GaAs) used during the RTA step has been investigated. A combination of cross-sectional transmission electron microscopy and high-resolution TEM together with energy-dispersive x-ray analysis directly reveals compositional and morphological changes resulting after QWI. Room temperature photoluminescence is used to establish the changes in emission wavelength and intensity resulting from the QW modification. Use of a Si proximity cap leads to the formation of a sharply defined QW with the least amount of QW modification, while GaAs proximity capping results in the broadest QW, broadened QW/barrier interfaces and the highest concentration of phosphorous in the well. Use of an InP proximity cap results in intermediate QW modification. In all cases, after QWI the bottoms of the QWs are flat and the well is square-like with broadened sidewalls. It has also been found that group V rich precipitates are produced on the top two layers of the structure. The concentration and size of the precipitates are minimum with Si proximity capping, while the use of GaAs produces the highest concentration and largest precipitates. The concentration and size of the precipitates are intermediate for the InP capping. The choice of proximity cap material affects the intermixing through changes of the effective P diffusivity which has been calculated from obtained profiles.
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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".