Synthesis and Analysis of Two-dimensional Surface Phases in the Bi/Si(111) System by Scanning Tunneling Microscopy and Density Functional Theory
Bibliographic record
Abstract
The Bi/Si(111) surface reconstruction is an attractive two-dimensional (2D) surface phase due to its compatibility with current Si-based electronic industry and due to its extraordinary Rashba spin splitting feature favorable for spintronics fabrication. However, detailed defect analysis and quantum dot creation in this system have not been reported. Here, using scanning tunneling microscopy (STM) and density functional theory (DFT), defects, quantum states and phase transformation mechanisms of the Bi/Si(111) system and the Pb/Bi/Si(111) system are studied separately. First, bias dependence and defects in the Bi/Si(111) √3×√3 β-phase are analyzed. It is confirmed that the honeycomb pattern at low-bias empty states and a hexagonal-closest-packed pattern at high-bias empty states originate from the Bi px, py states and Bi, Si overlapped states outside bulk bandgap, respectively. Analysis of the defect images and their associated densities of states provides further insight into the electronic structure of the surface. Second, crystal structure and quantum well states (QWSs) of the Pb/Bi/Si(111) 2D phase are researched. The atomic structure of the system is confirmed by STM images combined with DFT calculations. QWSs and a sizable Rashba band splitting are predicted with the latter comparable to what is found in other semiconductor heterostructures and an order of magnitude higher than that in Pb/Si(111) QWSs. Finally, quantum dot states (QDSs) are discovered in the √3×√3-Bi/Si(111) surface phases. The structure, energy dispersion, and size effect on the electronic states of the QDs are measured and verified. As-created QDs can be manipulated with a dot size down to 2 nm via Bi phase transformation, which in turn is triggered by thermal annealing at 700K. The transition mechanism is also supported by DFT calculations, and an empirical analytical model is developed to predict the transformation kinetics.
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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".