Defect characterization of CdTe thin films using a slow positron beam
Bibliographic record
Abstract
Abstract Cadmium Telluride (CdTe) is the most well established II–VI compound largely due to its use as a photonic material. Existing applications, as well as those under consideration, are demanding increasingly stringent control of the material properties. The deposition of high quality thin films is of the utmost importance to such applications. In this regard, we present a report detailing the role of lattice mismatch in determining the film quality. Thin films were deposited on a wide variety of substrate materials using the pulsed laser deposition technique. Common to all substrates was the strong tendency towards the preferential alignment of CdTe's (111) planes parallel to the substrate's surface. X‐ray diffraction analysis, however, revealed that the crystalline quality varied dramatically depending upon the substrate used with the best results yielding a single crystal film. This tendency also manifested itself in the surface morphology with higher structural perfection yielding smoother surfaces. Slow positron beam techniques revealed a strong correlation between the defect concentration and the degree of structural perfection. Simulations of the data using the POSTRAP 5 program were used to calculate the defect concentration in relative (atom‐1) and absolute units and to determine the diffusion lengths of the positrons in the film. All of these characterization techniques point towards lattice mismatch as being the dominant mechanism in determining the quality of CdTe films. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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".