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Record W4392405845 · doi:10.1109/tsm.2024.3372521

Fabrication of the Highly Ordered Silicon Nanocone Array With Sub-5 nm Tip Apex by Tapered Silicon Oxide Mask

2024· article· en· W4392405845 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Semiconductor Manufacturing · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAnodic Oxide Films and Nanostructures
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSiliconMaterials scienceFabricationOptoelectronicsApex (geometry)Silicon oxideOxideHybrid silicon laserNanotechnologySilicon nitrideMetallurgy

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.210
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it