Scalable InAs/InGaAs DWELL structures for broadband infrared emission spanning the E- to O-band
Bibliographic record
Abstract
This study presents the optimization of MOCVD growth conditions for InAs/InGaAs quantum dots-in-a-well (DWELL) structures on 4-inch GaAs substrates incorporating an InGaP seed layer. By precisely tuning the arsenic-to-indium (As/In) ratio, growth temperature, and deposition duration, we achieved accurate control over the size and density of quantum dots (QDs), enabling a broad tuning of infrared emission wavelengths from 1200 to 1450 nm. Using photoluminescence spectroscopy and numerical modeling, we investigated the impact of In content of the InₓGa 1− ₓAs strain-reducing cap layers on the optical properties of the DWELL structures. A linear correlation was observed between In concentration and the emission peak wavelength, highlighting the significant role of the capping layer composition in tuning QD emission characteristics. This work highlights the critical importance of growth parameter optimization in engineering QD-based heterostructures paving the way for their integration into advanced optoelectronic and quantum technology applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".