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Record W2999547713 · doi:10.1116/1.5123601

Effects of mask material conductivity on lateral undercut etching in silicon nano-pillar fabrication

2020· article· en· W2999547713 on OpenAlexaff
Ripon Kumar Dey, Huseyin Ekinci, Bo Cui

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2020
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsUndercutEtching (microfabrication)SiliconMaterials scienceFabricationResistReactive-ion etchingElectron-beam lithographyLithographyIsotropic etchingDry etchingNanotechnologyOxideSilicon oxideChromiumOptoelectronicsMetallurgyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

High aspect ratio silicon structures have gained significant interest due to their vast applications. Minimal lateral etch under the mask is essential to achieve such high aspect ratio structures. Previously, the authors reported that chromium oxide is better than metallic chromium as a hard mask for silicon etching in terms of etch rate and selectivity to resist during mask structure fabrication. Here, it is reported that a metal oxide etch mask also gives less lateral etch than a metal etch mask. Following mask structure fabrication by electron beam lithography and lift-off, silicon was etched using a nonswitching (i.e., SF6 and C4F8 gases simultaneously injected into a chamber) pseudo-Bosch process. The amount of lateral etching right underneath the mask is less (roughly half) for Cr2O3 and Al2O3 masks than Cr or Al masks. One plausible explanation for the difference is the metal-assisted plasma etching effect where the metal catalyzes the chemical reaction by injecting holes into the silicon in contact. It is also reported that a higher bias power leads to less undercut than a lower one, due to increased and more directional physical bombardment by ions.

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 score0.018
Threshold uncertainty score0.692

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 studies0.0000.000
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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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