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Record W2049678342 · doi:10.1149/1.2982906

Hybrid Bonding (Plasma activation and Anodic bonding) for Vacuum Sealing

2008· article· en· W2049678342 on OpenAlexaff
Akira Yamauchi, Johji Kagami, Hironao Okada, Tadatomo Suga

Bibliographic record

VenueECS Transactions · 2008
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsAnodic bondingPlasmaMicroelectromechanical systemsAnodeMaterials scienceWire bondingComposite materialNanotechnologyChemistryLayer (electronics)Electrical engineeringElectrodePhysical chemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

Low temperature bonding by means of plasma activated bonding has been proposed. However there are the following problems for the mass-production of devices. 1) Voids occur due to small particles remaining at the interface, 2) Low bond strength in case of bonding in a vacuum. In this article we introduce the 'Hybrid Bonding' method, a combination of plasma activation and anodic bonding, which may overcome the problems and be suitable for mass-production of MEMS devices.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.213
Teacher spread0.193 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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