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The Thickness-Ratio Effects of Ni/Nb Electrode on Wire Bonding Strength with N-Type 4H-SiC

2012· article· en· W2047848680 on OpenAlexaff
Kunhwa Jung, Daisuke Ando, Yuji Sutou, Tetsuya OYAMADA, Masamoto Tanaka, Junichi Koike

Bibliographic record

VenueMaterials science forum · 2012
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMaterials scienceElectrodeMicrostructureOhmic contactAnnealing (glass)CarbideComposite materialMetallurgyLayer (electronics)

Abstract

fetched live from OpenAlex

The thickness-ratio effects of Ni/Nb bi-layer electrodes were studied for power device applications. The reaction microstructure and electrical contact property were investigated after annealing at 1000 °C and compared with the results of an Ni electrode. Microstructure-related problems of the Ni electrode could be successfully resolved without sacrificing ohmic contact behavior by the addition of Nb to a Ni based electrode. Carbon precipitation was reduced with increasing Nb thickness by the formation of carbides, which led to good adhesion between the electrode and a wiring pad. High shear strength of the bonded wire was also obtained by the elimination of the carbon precipitates on the electrode surface.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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