Effects of grown-in defects on interdiffusion dynamics in InAs∕InP(001) quantum dots subjected to rapid thermal annealing
Bibliographic record
Abstract
This work investigates the interdiffusion dynamics in self-assembled InAs∕InP(001) quantum dots (QDs) subjected to rapid thermal annealing in the 600–775°C temperature range. We compare two QD samples capped with InP grown at either optimal or reduced temperature to induce grown-in defects. Atomic interdiffusion is assessed by using photoluminescence measurements in conjunction with tight-binding calculations. By assuming Fickian diffusion, the interdiffusion lengths LI are determined as a function of annealing conditions from the comparison of the measured optical transition energies with those calculated for InP∕InAs1−xPx∕InP quantum wells with graded interfaces. LI values are then analyzed using a one-dimensional interdiffusion model that accounts for both the transport of nonequilibrium concentrations of P interstitials from the InP capping layer to the InAs active region and the P–As substitution in the QD vicinity. It is demonstrated that each process is characterized by a diffusion coefficient D(i) given by D(i)=D0(i)exp(−Ea(i)∕kBTa). The activation energy and pre-exponential factor for P interstitial diffusion in the InP matrix are Ea(P–InP)=2.7±0.3eV and D0(P–InP)=103.6±0.9cm2s−1, which are independent of the InP growth conditions. For the P–As substitution process, Ea(P–As)=2.3±0.2eV and (co∕no)D0(P–As)∼10−5−10−4cm2s−1, which depend on the QD height and concentration of grown-in defects (co∕no).
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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".