Combined atomic force microscopy and photoluminescence imaging to select single InAs/GaAs quantum dots for quantum photonic devices
Bibliographic record
Abstract
We report on a combined photoluminescence imaging and atomic force microscopy study of single, isolated self-assembled InAs quantum dots. The motivation of this work is to determine an approach that allows to assess single quantum dots as candidates for quantum nanophotonic devices. By combining optical and scanning probe characterization techniques, we find that single quantum dots often appear in the vicinity of comparatively large topographic features. Despite this, the quantum dots generally do not exhibit significant differences in their non-resonantly pumped emission spectra in comparison to quantum dots appearing in defect-free regions, and this behavior is observed across multiple wafers produced in different growth chambers. Such large surface features are nevertheless a detriment to applications in which single quantum dots are embedded within nanofabricated photonic devices: they are likely to cause large spectral shifts in the wavelength of cavity modes designed to resonantly enhance the quantum dot emission, thereby resulting in a nominally perfectly-fabricated single quantum dot device failing to behave in accordance with design. We anticipate that the approach of screening quantum dots not only based on their optical properties, but also their surrounding surface topographies, will be necessary to improve the yield of single quantum dot nanophotonic devices.
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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 | 0.001 | 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".