Quantum confinement in Si and Ge nanostructures: Theory and experiment
Bibliographic record
Abstract
The role of quantum confinement (QC) in Si and Ge nanostructures (NSs) including quantum dots, quantum wires, and quantum wells is assessed under a wide variety of fabrication methods in terms of both their structural and optical properties. Structural properties include interface states, defect states in a matrix material, and stress, all of which alter the electronic states and hence the measured optical properties. We demonstrate how variations in the fabrication method lead to differences in the NS properties, where the most relevant parameters for each type of fabrication method are highlighted. Si embedded in, or layered between, SiO2, and the role of the sub-oxide interface states embodies much of the discussion. Other matrix materials include Si3N4 and Al2O3. Si NSs exhibit a complicated optical spectrum, because the coupling between the interface states and the confined carriers manifests with varying magnitude depending on the dimension of confinement. Ge NSs do not produce well-defined luminescence due to confined carriers, because of the strong influence from oxygen vacancy defect states. Variations in Si and Ge NS properties are considered in terms of different theoretical models of QC (effective mass approximation, tight binding method, and pseudopotential method). For each theoretical model, we discuss the treatment of the relevant experimental parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".