Formation technology of flat surface with epitaxial growth on ion-implanted (100)-oriented Si surface of thin silicon-on-insulator
Bibliographic record
Abstract
Abstract For the development of three-dimensional devices, selective epitaxial growth (SEG) technology has attracted much attention. SEG has been applied to fabricate many devices and it is expected to be used in future manufacturing processes. Therefore, its characteristics must be examined in detail to extend its application. For the fabrication of a three-dimensional device structure, the selectivity of epitaxial growth must be accurately controlled not only on Si and SiO 2 , but also on different impurity-type silicon surfaces. In this work, we investigated some characteristics of the SEG process, especially focusing on the surface roughness after SEG. Both vapor phase epitaxy (VPE) and solid phase epitaxy (SPE) were performed on ion-implanted silicon-on-insulator (SOI) thin wafers. It was often reported that epitaxial growth is very sensitive to the crystal condition of the substrate on which the films are deposited. However, we first revealed that the impurity type (p- or n-type) and its concentration at the substrate surface markedly changed the roughness and incubation times of the deposition. From our results, SPE with the oxide cap layer formation is effective for maintaining almost the same flatness as the original wafer surface. It is also effective to employ the low-temperature H 2 /Xe plasma treatment after the SEG to reduce roughness.
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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".