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Comprehensive investigation of sequential plasma activated Si/Si bonded interfaces for nano-integration on the wafer scale

2010· article· en· W2119741953 on OpenAlexafffund
Md Golam Kibria, F Zhang, T.-H. Lee, Moon J. Kim, M.M.R. Howlader

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Texas at DallasMcMaster University
KeywordsMaterials scienceWaferNanostructureNanotechnologyNanowireNanometreReactive-ion etchingEtching (microfabrication)Plasma etchingPlasmaSiliconContact angleWafer bondingOptoelectronicsComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

The sequentially plasma activated bonding of silicon wafers has been investigated to facilitate the development of chemical free, room temperature and spontaneous bonding required for nanostructure integration on the wafer scale. The contact angle of the surface and the electrical and nanostructural behavior of the interface have been studied. The contact angle measurements show that the sequentially plasma (reactive ion etching plasma followed by microwave radicals) treated surfaces offer highly reactive and hydrophilic surfaces. These highly reactive surfaces allow spontaneous integration at the nanometer scale without any chemicals, external pressure or heating. Electrical characteristics show that the current transportation across the nanobonded interface is dependent on the plasma parameters. High resolution transmission electron microscopy results confirm nanometer scale bonding which is needed for the integration of nanostructures. The findings can be applied in spontaneous integration of nanostructures such as nanowires/nanotubes/quantum dots on the wafer scale.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.020
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 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

Citations35
Published2010
Admission routes2
Has abstractyes

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