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Record W2539202396 · doi:10.1116/1.4964402

Silicon nanostructures with very large negatively tapered profile by inductively coupled plasma-RIE

2016· article· en· W2539202396 on OpenAlexafffund
Asma Ayari-Kanoun, Ferhat Aydinoglu, Bo Cui, Fayçal Saffih

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2016
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationCanada Foundation for Innovation
KeywordsUndercutEtching (microfabrication)Materials scienceReactive-ion etchingSiliconInductively coupled plasmaDry etchingPlasmaPlasma etchingFabricationOptoelectronicsNanotechnologyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Dry etching of silicon has been extensively studied, mostly with a goal of obtaining perfectly vertical sidewalls with high aspect ratio. Yet, sloped sidewall with a negative taper angle (i.e., diameter/width decreases linearly with depth) may find various applications. However, the systematic study on the etching process development to obtain such a profile is rather scarce. In this work, the authors present a controlled and reproducible fabrication process to achieve silicon nanostructures with negatively tapered sidewall profile using inductively coupled plasma-reactive ion etching with C4F8 and SF6 gas. The plasma etching parameters have been thoroughly optimized in order to avoid the undercut or curved reentrant profile due to isotropic etching, so as to achieve a negatively tapered profile. The influence of the plasma etching parameters, especially the radio freguency power and C4F8/SF6 gas flow ratio, on the etching rate and the sidewall taper angle has been analyzed. With an optimal etching recipe, the silicon nanostructures with an unprecedented large 10° negative taper angle were achieved. These results were demonstrated on different structure sizes of 500 nm, 700 nm, and 1.2 μm diameters.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.032
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2016
Admission routes2
Has abstractyes

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