Excimer laser crystallization of a-SiC<sub><i>x</i></sub> on glass
Bibliographic record
Abstract
Silicon carbide (SiC) is a promising material for the fabrication of optoelectronic devices. In the amorphous state physical-vapor-deposited SiC is already an essential part of the production of electron beam liquid phase crystallized silicon absorbers, because of its good wettability for the silicon melt. The resulting polycrystalline thin film absorbers exhibit excellent electrical properties (B. Eggleston et al. MRS Online Proc. Libr. 1426, 251 (2012)) and demonstrate the potential of this technology (J. Dore et al. IEEE J. Photovolt. 4, 33 (2014)). A drawback of amorphous SiC interlayers is their poor optical properties. In this work we investigate two different approaches. Firstly, we show how those properties can be improved by depositing SiCxOy layers to enhance the transmittance while maintaining the wettability. Secondly, the laser crystallization of amorphous silicon–carbon compounds is studied as a function of their composition and the applied laser fluence. Compared to the amorphous SiC layers a clear reduction of the absorption coefficient was achieved upon laser annealing without losing wettability. Raman backscattering measurements showed that laser annealing of reactively sputtered amorphous SiC leads to a material with a vibrational mode at 770 cm−1 that is indicative of 6H–SiC. Atomic force microscopy measurements of those crystalline layers also show an ordered triangular grain structure, which was not observed in the amorphous material. It is shown that segregation effects are strongly affected by the stoichiometry and the applied laser fluence.
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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".