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Record W2048072083 · doi:10.1116/1.4901420

Fabrication of silicon nanostructures with large taper angle by reactive ion etching

2014· article· en· W2048072083 on OpenAlexaff
Fayçal Saffih, Celal Con, Alanoud Alshammari, Mustafa Yavuz, Bo Cui

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2014
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEtching (microfabrication)Reactive-ion etchingMaterials scienceSiliconFabricationOptoelectronicsOpticsDry etchingBlack siliconIsotropic etchingDiodeContact angleEngravingNanotechnologyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Micro- and nanostructures with a tapered sidewall profile are important for antireflection and light trapping applications in solar cell, light emitting diode, and photodetector/imager. Here, the authors will show two etching processes that offer a large taper angle. The first process involved a mask-less etching of pre-etched silicon structures having a vertical profile, using a recipe that would give a vertical profile when masked. The authors obtained a moderate taper angle of 14° using CF4/O2 etching gas. The second process involved a one-step etching step with Cr as mask using a recipe that was drastically modified from a nonswitching pseudo-Bosch process that gives a vertical profile. The gas flow ratio of C4F8/SF6 was greatly increased from 38/22 to 59/1 to result in a taper angle of 22°. Further reduction of the RF bias power led to an unprecedented large taper angle of 39° (at the cost of greatly reduced etching rate), which is even higher than the angle obtained by anisotropic wet etching of silicon.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.004
GPT teacher head0.192
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 source (direct Gemma or distilled Codex), 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

Citations39
Published2014
Admission routes1
Has abstractyes

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