Fundamental transport properties in silicon quantum structures
Bibliographic record
Abstract
In the field of silicon photonics, there is an effort to bridge the gap between electrical and optical signals on a single platform, creating a need for Si-based light sources. In this project, Si quantum structures – Si quantum wells and quantum dots in SiO2 were fabricated via solid state precipitation methods. Their properties were studied using X-ray photoelectron spectroscopy, photoluminescence and I-V measurements. Rutherford backscattering spectroscopy was used for depth analysis in monitoring the Si distribution. Different electrical transport mechanisms were explored to understand how an ensemble of silicon QD’s or a silicon quantum well behaves in an SiO2 matrix, with conduction via oxide tunneling and hopping effects. Additionally, we quantified the defect density in epitaxially-grown Si and Ge thin films via RBS channeling, and correlated it with the Debye Temperature measured via low energy electron diffraction to assess the potential use of LEED as a technique for defect analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".