Growth of CdTe∕Si(100) thin films by pulsed laser deposition for photonic applications
Bibliographic record
Abstract
Cadmium telluride (CdTe) has become one of the most useful and versatile photonic materials. While many applications require the highest quality material possible, others can sacrifice some degree of sample quality in favor of reduced fabrication costs. Here, we explore the deposition of CdTe thin films deposited on (100) silicon substrates in the absence of costly ultrahigh vacuum conditions. This inevitably leads to the deposition of films on silicon’s native oxide. The work presented here explores the trade-offs in sample quality as determined by a host of characterization techniques. The films were deposited using the pulsed laser deposition technique at relatively low growth rates and at an optimized substrate temperature of 300°C. X-ray diffraction data show only (111) oriented CdTe with rocking curve widths broadened due to the lack of an epitaxial relationship between the film and substrate. Atomic force microscopy images confirm that the films have a smooth surface morphology as was suggested by their mirrorlike appearance. Film quality was also analyzed using photoluminescence, positron annihilation spectroscopy, and ellipsometry. While structural deficiencies have been observed in these films, the optical properties are remarkably similar to those expected for high quality CdTe.
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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".