Bismuth surfactant-enhanced III-As epitaxy on GaAs(111)A
Bibliographic record
Abstract
Abstract Quantum dot (QD) growth on high ( c 3 v ) symmetry GaAs{111} surfaces holds promise for efficient entangled photon sources. Unfortunately, homoepitaxy on GaAs{111} surfaces suffers from surface roughness/defects and InAs deposition does not natively support Stranski–Krastanov QD growth. Surfactants have been identified as effective tools to alter the epitaxial growth process of III-V materials, however, their use remains unexplored on GaAs{111}. Here, we investigate Bi as a surfactant in III-As molecular beam epitaxy on GaAs(111)A substrates, demonstrating that Bi can eliminate surface defects/hillocks in GaAs and (Al,Ga)As layers, yielding atomically-smooth hillock-free surfaces with RMS roughness values as low as 0.13 nm. Increasing Bi fluxes are found to result in smoother surfaces and Bi is observed to increase adatom diffusion. The Bi surfactant is also shown to trigger a morphological transition in InAs/GaAs(111)A films, directing the 2D InAs layer to rearrange into 3D nanostructures, which are promising candidates for high-symmetry QDs. The desorption activation energy ( U D e s ) of Bi on GaAs(111)A was measured by reflection high energy electron diffraction, yielding U D e s = 1.7 ± 0.4 eV. These results illustrate the potential of Bi surfactants on GaAs(111)A and will help pave the way for GaAs(111)A as a platform for technological applications including quantum photonics.
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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".