Indium arsenide single quantum dash morphology and composition for wavelength tuning in quantum dash lasers
Bibliographic record
Abstract
InAs quantum dot and dash gain media demonstrate performance benefits, such as lower threshold current densities and reduced temperature sensitivity over quantum wells for lasers operating in the C-band telecommunications window. Quantum dashes are of much interest for their higher gain over quantum dots due to an increased density of states. We combine experimental results and simulations to understand how quantum dash morphology and composition can be used to tune the emission wavelengths of these nanoparticles. Atomic force microscopy (AFM) analysis is performed to determine the effect of growth temperature and sublayer type on InAs/InGaAsP/InP nanoparticle morphology and homogeneity. Uncapped InAs nanoparticles grown by CBE on a GaAs sublayer will have dash-like geometries with heights up to 2.36 nm for growth temperatures of 500–540 °C. GaP sublayers will induce taller quantum dots except for a growth temperature of 530 °C, where quantum dashes form. The dimensions extracted from AFM scans are used in conjunction with photoluminescence data to guide parabolic band simulations of an InAs quantum dash with a GaP or GaAs sublayer and InP cap buried within InGaAsP. The calculated emission energy of a buried 30 × 300 nm quantum dash decreases by ∼100 meV for increasing heights from 1.5 to 2.5 nm, or increases by ∼100 meV by addition of 20% phosphorus in the dash and wetting layers. Modifying the quantum dash height and leveraging the As/P intermixing that occurs between the InAs and InP layers are, thus, most effective for wavelength tuning.
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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".