Material characterization of silicon thin-films grown by ultra-high-vacuum evaporation
Bibliographic record
Abstract
This thesis draws upon experimental data sets harvested from experiments performed on a collection of ultra-high-vacuum evaporation-prepared thin silicon film samples, these experiments being performed by researchers from the National Research Council of Canada. A molecular beam epitaxy deposition set-up was commissioned for the thin-film preparations, the hydrogen content found within these thin-films being considerably less than those prepared through the use of conventional thin-film silicon preparation techniques. For this reason, we suspect the ultra-high-vacuum evaporation prepared thin silicon films will be less susceptible to the instability that affects more conventional forms of thin-film silicon. These thin-film silicon samples are grown for a number of different growth temperatures on a number of substrate selections. Characterizations are performed, including those based on grazing incidence X-ray diffraction and Raman spectroscopy, these probing each thin silicon film’s microstructure. Noting that differences in processing thin-film silicon’s Raman spectral data can lead to quantitative differences in the Raman-related metrics that arise as a corollary, a series of critical steps for processing Raman spectral data associated with thin-film silicon are suggested. In order to provide some sense as to how these steps influence the form of the Raman spectrum, the post-processing steps are performed on representative thin-film silicon Raman spectral data sets. The processed data is then further analyzed in parallel with the grazing incidence X-ray diffraction data. From the diffraction patterns, through applying Scherrer’s equation, the crystallite dimensions’ dependence on the growth temperature is resolved for each considered thin silicon film. From the Raman spectral decompositions, the location, breadth, and character of each identified peak is noted, the evolution of these decompositions in response to growth temperature variations being examined for the different substrate selections. Finally, following some general discussion on the results, where these thin-films of silicon can be placed into the thin-film silicon continuum is examined.
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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 | 0.000 | 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) | 0.000 | 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".