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Record W2204753321 · doi:10.1149/06906.0079ecst

Combined Surface-Activated Bonding Technique for Low-Temperature Cu/SiO<sub>2</sub> Hybrid Bonding

2015· article· en· W2204753321 on OpenAlexaff
Ran He, Masahisa Fujino, Akira Yamauchi, Tadatomo Suga

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsAnodic bondingDirect bondingChemisorptionMaterials scienceWafer bondingWaferThermocompression bondingIonBond energyComposite materialChemistryAdsorptionNanotechnologyLayer (electronics)MoleculePhysical chemistry

Abstract

fetched live from OpenAlex

This work develops a combined surface-activated bonding (SAB) technique for low-temperature SiO 2 -SiO 2 and Cu-Cu bonding at 200 °C. The combined SAB technique involves combinations of surface activation by using surface bombardment by neutralized Ar ion beam containing Si atoms, water vapor exposure, and prebonding attach/detach prior to wafer bonding in vacuum. Bonding strength close to Si bulk fracture energy was achieved for both SiO 2 -SiO 2 and Cu-Cu bonding pairs. We suggest the enhanced –OH (both Si–OH and Cu–OH) chemisorption on the wafers and pre-bonding removal of excess H 2 O from the interface/surfaces are the key factors involved in the present low-temperature bonding technique. This technique is promising for 3D integration through Cu/SiO 2 hybrid bonding at no more than 200 °C.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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