Hydrogenated microcrystalline silicon thin film growth in the DC saddle field PECVD system
Bibliographic record
Abstract
Hydrogenated amorphous silicon (a-Si:H) and microcrystalline silicon (muc-Si:H) thin films were grown at the threshold of the amorphous-to-microcrystalline phase transition in a modified direct current (DC) saddle field plasma enhanced chemical vapour deposition (SF-PECVD) system. For the first time, the microcrystalline content of the resulting films was successfully controlled through direct manipulation of the substrate surface bias during growth. This effect was quantified for multiple pressures. The as-grown microcrystalline content and the laser-induced crystallization of the films was quantified using Raman scattering spectroscopy. The optoelectronic and morphological properties of the films were measured. These measurements included UV-Vis and IR spectroscopy, temperature-dependent four-point conductivity, photoluminescence, atomic force microscopy, and spectroscopic ellipsometry. All measurements correlated well with the microcrystalline content of the films. The growth of muc-Si:H in an ion-bombarding environment was modeled using a general formalism applicable to all methods of muc-Si:H growth. The simulation results of the model showed good qualitative agreement to experimental findings from this work as well as the literature. The operation of the SF-PECVD system using a large-area externally applied substrate bias was examined under varying conditions and new limitations to the large area application of substrate bias were discovered. Three previously unidentified plasma operation regimes were identified that explain the role of substrate bias in controlling microcrystalline growth. A methodology for the design of SF-PECVD systems for the growth of muc-Si:H was described.
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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".