(Invited) Surfactant-Directed Quantum Dot Self-Assembly on Unconventional GaAs Surfaces
Bibliographic record
Abstract
The pursuit of integrated sources for quantum optics has motivated quantum dot research for decades. However, difficulties in controlling the geometry, symmetry and size-uniformity of self-assembled quantum dots has been a major roadblock in device development. While the Stranski–Krastanov (SK) growth of InAs three-dimensional (3D) islands has been extensively investigated on GaAs(001), the SK growth mode is not observed on GaAs (110) and {111}, the other low index surfaces of GaAs. This is unfortunate as these surfaces have symmetries (C s and C 3v , respectively) which differ from that of the (001) surface, and owing to their low energy they are often present in self-assembled nanostructures such as nanowires. Furthermore, the high symmetry of {111} surfaces is expected to yield quantum dots that are ideal for entangled photon emission. Here we show that using a Bi surfactant to modify surface properties can profoundly influence epitaxial growth on GaAs (110) and (111)A surfaces. On GaAs(110), the Bi surfactant alters the fundamental growth mode of InAs from 2D layer growth to a 3D SK mode. Furthermore, a morphological phase transition can be induced “on-demand” in static strained 2D InAs(110) layers by exposing them to Bi, resulting in a rapid rearrangement of the InAs layer into 3D islands. Small (110) QDs are coherently strained to the substrate. These islands are optically active and exhibit emission that is linearly polarized, showing perspective for polarized single-photon emitters. On GaAs(111)A, GaAs buffer layer growth under a Bi flux results in ultra-smooth surfaces, which are free from the typically-observed morphological defects. Similar to the (110) case, exposing 2D InAs/GaAs(111)A layers to Bi induces the InAs 3D island self-assembly. These findings illustrate how surface-energy-modifying surfactants open the door to QDs synthesis on new substrates and with new materials.
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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 | 0.000 | 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) | 0.000 | 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".