Dynamical evolution of Ge quantum dots on Si(111): From island formation to high temperature decay
Bibliographic record
Abstract
Abstract Heteroepitaxial growth is a process of profound fundamental importance as well as an avenue to realize nanostructures such as Ge/Si quantum dots (QDs), with appealing properties for applications in opto‐ and nanoelectronics. However, controlling the Ge/Si QD size, shape, and composition remains a major obstacle to their practical implementation. Here, Ge nanostructures on Si(111) were investigated in situ and in real‐time by low energy electron microscopy (LEEM), enabling the observation of the transition from wetting layer formation to 3D island growth and decay. The island size, shape, and distribution depend strongly on the growth temperature. As the deposition temperature increases, the islands become larger and sparser, consistent with Brownian nucleation and capture dynamics. At 550°C, two distinct Ge/Si nanostructures are formed with bright and dark appearances that correspond to flat, atoll‐like and tall, faceted islands, respectively. During annealing, the faceted islands increase in size at the expense of the flat ones, indicating that the faceted islands are thermodynamically more stable. In contrast, triangular islands with uniform morphology are obtained from deposition at 600°C, suggesting that the growth more closely follows the ideal shape. During annealing, the islands formed at 600°C initially show no change in morphology and size and then rupture simultaneously, signaling a homogeneous chemical potential of the islands. These observations reveal the role of dynamics and energetics in the evolution of Ge/Si QDs, which can serve as a step towards the precise control over the Ge nanostructure size, shape, composition, and distribution on Si(111).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".