Ordered silicon nanocone fabrication by using pseudo-Bosch process and maskless etching
Bibliographic record
Abstract
Nanocone arrays are widely employed for applications such as antireflection structures and field emission devices. Silicon nanocones are typically obtained by an etching process, but the profile is hard to attain because anisotropic dry etching generally gives vertical or only slightly tapered sidewall profiles, and isotropic dry plasma etching gives curved sidewalls. In this work, we report the fabrication of cone structures by using masked etching followed by maskless etching techniques. The silicon structure is first etched using fluorine-based plasma under the protection of a hard metal mask, with a tapered or vertical sidewall profile. The mask is then removed, and maskless etching with an optimized nonswitching pseudo-Bosch recipe is applied to achieve the cone structure with a sharp apex. The gas flow ratio of C4F8 and SF6 is significantly increased from 38:22 (which creates a vertical profile) to 56:4, creating a taper angle of approximately 80°. After subsequent maskless etching, the sidewall taper angle is decreased to 74°, and the structure is sharpened to give a pointed apex. The effect of an oxygen cleaning step is also studied. With the introduction of periodic oxygen plasma cleaning steps, both the etch rate and surface smoothness are greatly improved. Lastly, it was found that the aspect ratio-dependent etching effect becomes prominent for dense patterns of cone arrays, with a greatly reduced etch depth at a 600 nm pitch array compared to a 1200 nm pitch array.
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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".