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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".