Fabrication of the Highly Ordered Silicon Nanocone Array With Sub-5 nm Tip Apex by Tapered Silicon Oxide Mask
Bibliographic record
Abstract
In view of the wide range of applications for ultra-sharp silicon (Si) nanocones, extensive research has been conducted on their fabrication processes. However, these conventional methods pose challenges in terms of achieving uniformity, controllability, and cost-efficiency. This study presents a novel approach to fabricating Si nanocone structures through reactive ion etching (RIE) using a tapered silicon dioxide mask, followed by thermal oxidation sharpening to reduce the apex diameter to 4 nm. Here the tapered SiO2 mask with a smooth sidewall was created through a combination of RIE and a buffered oxide etchant (BOE) etching. The lithography of the oxide mask is achieved using a cost-effective (compared to electron beam lithography) maskless aligner system (MLA). Subsequently, a non-switching pseudo-Bosch process, employing sulfur hexafluoride (SF6) gas and octafluorocyclobutane (C4F8) gas, is utilized for the etching the Si nanocone structures, resulting in an average apex diameter of 30 nm. Finally, thermal oxidation followed by oxide removal further sharpens these cones to 4 nm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".