Effect of layer separation, InAs thickness, and rapid thermal annealing on the optical emission from a multi-layer quantum wire structure
Bibliographic record
Abstract
Multi-layer InAs quantum wire stacks with different layer separations (8, 15, and 25 nm) and InAs thicknesses (3, 4, 5, and 7 monolayers [ML]) were grown on and embedded in In0.53Ga0.27Al0.20As barrier/spacer layers lattice-matched to an InP substrate. For the samples with 4 ML of InAs and different layer separations, double peak photoluminescence was observed from quantum wire stacks separated by 8 nm, and with a 15 nm spacer layer a long wavelength component was observed extending from the main peak. Only a single peak was found as the spacer layer thickness was increased to 25 nm. For the quantum wire stacks with different InAs layer thicknesses and a separation of 8 nm, double peak photoluminescence spectra were observed in the sample with 4 ML of InAs, and a main peak with a long wavelength component was obtained from the sample with 3 ML of InAs. Only a single peak was detectable for the InAs layer thicknesses of 5 and 7 ML. The optical emission features were studied via temperature and excitation laser power dependent photoluminescence. Based on the photoluminescence and transmission electron microscopy observations, photoluminescence spectral features can be attributed to a bi-modal height distribution in certain samples. In order to extend the optical emission to room temperature, the sample with 5 ML of InAs and an 8 nm spacer layer was subjected to post-growth rapid thermal annealing at different temperatures. The emission wavelength was tunable from 1.63 to 1.72 μm at room temperature.
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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".