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Record W2997915331 · doi:10.1116/1.5122822

Effect of oxygen plasma cleaning on nonswitching pseudo-Bosch etching of high aspect ratio silicon pillars

2020· article· en· W2997915331 on OpenAlexafffund
Ferhat Aydinoglu, Aixi Pan, Chenxu Zhu, Bo Cui

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2020
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Waterloo
FundersCanada First Research Excellence FundCanada Foundation for Innovation
KeywordsEtching (microfabrication)Materials scienceDry etchingReactive-ion etchingSiliconPassivationPlasmaLayer (electronics)Plasma etchingFabricationOptoelectronicsNanotechnologyAnalytical Chemistry (journal)Chemistry

Abstract

fetched live from OpenAlex

In dry plasma silicon etching, it is desired to have a high etching rate, a high etching selectivity to mask material, a vertical or controllable sidewall profile, and a smooth sidewall. Since the standard Bosch process (switching between SF6 and C4F8 gases) leads to a wavy/rough sidewall profile, the nonswitching pseudo-Bosch process is developed to give a smooth sidewall needed for nanostructure fabrication. In the process, SF6 and C4F8 gases are introduced to the chamber simultaneously. Here, the authors show that by introducing a periodic oxygen (O2) plasma cleaning step, that is, switching between SF6/C4F8 etching and O2 cleaning, the silicon etching rate can be significantly improved (by up to ∼55%, from 139 to 216 nm/min) without any adverse effect. This is mainly because O2 plasma can remove the fluorocarbon polymer passivation layer at the surface. The etching and cleaning step durations were varied from 5 s to 40 min and from 0 to 60 s, respectively. The fastest etching rates were obtained when the cleaning step takes roughly 10% of the total etching time.

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)0.002
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 score0.012
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.215
Teacher spread0.205 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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