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Record W2029637008 · doi:10.1143/jjap.47.2526

Void-Free Room-Temperature Silicon Wafer Direct Bonding Using Sequential Plasma Activation

2008· article· en· W2029637008 on OpenAlexfundno aff
Chenxi Wang, Eiji Higurashi, Tadatomo Suga

Bibliographic record

VenueJapanese Journal of Applied Physics · 2008
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWaferAnodic bondingPlasma activationMaterials scienceAnnealing (glass)Void (composites)Reactive-ion etchingWafer bondingDirect bondingMicroelectromechanical systemsSiliconComposite materialPlasmaPlasma cleaningEtching (microfabrication)Analytical Chemistry (journal)OptoelectronicsChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

Room-temperature Si/Si wafer direct bonding has been performed by an optimized sequential plasma activated bonding process. A shorter O 2 reactive ion etching (RIE) plasma (∼10 s) treatment followed by treatment with N 2 radicals for 60 s is used for surface activation. The activated wafers are brought into contact in ambient air. After storage at room temperature for 24 h, high bonding strength (∼2.25 J/m 2 ) is achieved without requiring any annealing process. This value is close to the bulk-fracture strength of silicon. Furthermore, no annealing voids are observed at Si/Si interfaces even if the bonded wafer pairs are heated from 200 to 800 °C in subsequent processes. The bonding interfaces and their optical transmittances are also investigated. This void-free, room-temperature bonding technique based on sequential plasma activation is inexpensive and suitable for the microelectromechanical system manufacturing process and wafer-scale packaging.

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 science0.0010.001
Research integrity0.0000.000
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 head0.018
GPT teacher head0.216
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 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

Citations36
Published2008
Admission routes1
Has abstractyes

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