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Record W2017967688 · doi:10.1116/1.3657517

Technology platform for the fabrication of titanium nanostructures

2011· article· en· W2017967688 on OpenAlexaff
Serge Ecoffey, Marc Guilmain, Jean-François Morissette, F. Bourque, Jérémy Pont, Bruno Lee Sang, Dominique Drouin

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsChemical-mechanical planarizationTitaniumMaterials scienceEtching (microfabrication)FabricationReactive-ion etchingNanotechnologyElectron-beam lithographyNanometreNanolithographyLithographyNanostructureCopper interconnectOptoelectronicsResistMetallurgyLayer (electronics)Composite material

Abstract

fetched live from OpenAlex

Abstract : This paper presents two approaches for the fabrication of top-down titanium nanostructures. The first approach involves electron beam lithography followed by a tailored titanium plasma etching. The two main challenges of this process lie in the optimization of the negative tone Ma–N electroresist resolution and in the definition of a controlled titanium etching process for titanium patterns less than 20 nm thick and wide. The second proposed approach is a damascene process where the titanium nanostructures are buried in the oxide. Very shallow and narrow (20 nm × 20 nm) trenches are first patterned in the oxide and nanostructures are obtained by planarization of an evaporated titanium film. The dimensions of the structures are defined by the electron beam lithography resolution and the etching recipe. The third dimension is given by the titanium or any other metal thickness and can be controlled down to few nanometers thanks to the planarization step.

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 score0.089
Threshold uncertainty score0.641

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 studies0.0000.002
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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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