Quantum dot intermixing with thermal and laser annealing
Bibliographic record
Abstract
Photoluminescence (PL) was used to investigate the interdifflision of self-assembled InAs/GaAs quantum dots (QDs) treated by rapid thermal annealing (RTA) and laser annealing. The observation ofintense and sharp shell structures confirmed that the QDs retained their zero-dimensional density of states. In addition, three main effects of alloy intermixing were demonstrated in QDs having different intersublevel spacings. The emission has been strongly blue-shifted, up to -200 meV for RTA samples and 298 meV for the laser annealed ones. The intersublevel spacing was tuned between 6O meV to '-'25 meV in the RTA case, but down to -12 meV in the case of laser-induced intermixing. Finally the inhomogeneous broadening linearly decreased from a FWHM of-46 meV down to smaller than 15 meV for RTA and 8 meV in the most extreme case of laser annealing. For samples annealed at the highest temperatures, the most energetic shells of QDs become unbound. Across varying samples, the result ofthe intermixing was to increase the uniformity of their PL spectra. A onedimensional model of Fickian diffusion for the growth direction was used to model their PL emission. Rapid thermal annealing and laser annealing provide two additional ways of manipulating the energy levels of self-assembled QD ensembles by tuning the intersublevel energy-spacing and the number ofconfined states.
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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".