MétaCan
Menu
Back to cohort
Record W2786354220 · doi:10.1109/eptc.2017.8277529

Factors affecting activation energy for pd-coated cu ball bond resistance degradation on Al bond pads in high temperature storage

2017· article· en· W2786354220 on OpenAlexaff
Michael David Hook, Stevan Hunter, M. Mayer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActivation energyMaterials scienceArrhenius equationComposite materialCopperDegradation (telecommunications)Bond energyForensic engineeringMetallurgyElectronic engineeringChemistryPhysical chemistryMolecule

Abstract

fetched live from OpenAlex

Copper wire bonds have become a mainstay in many electronics areas, including automotive, where more data is needed regarding the reliability of Cu wire bonds on Al pads in high temperature conditions. Arrhenius models are frequently used to estimate product lifetimes, with the activation energy being the main parameter. Activation energies from 0.46 eV to 1.34 eV have been reported in the literature for similar materials and aging conditions. This apparent inconsistency between the results of different studies indicates one or more failure mechanisms that are not active in all cases, with causes that have not been clearly identified. To obtain uniformly high reliability, it is important to identify and eliminate the causes of rapid degradation observed by some authors. This work investigates the effects of Al pad metallization thickness and the choice of failure criterion on the bond resistance and activation energy for Pd-coated Cu (PCC) ball bonds with epoxy encapsulation. By testing samples at 175 °C, 200 °C, and 225 °C, with pad thicknesses of 800 nm and 3000 nm, we found that at least two mechanisms contributed to resistance changes. The first mechanism has an activation energy of about 0.7 eV, calculated using the times required for a 1 % increase in resistance. The second, calculated from the times until a 10 % resistance increase, has an activation energy of about 0.4 eV. Pad thickness did not have a significant effect on activation energy, but thicker pads did lead to slower increases in resistance at the beginning of aging.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.071
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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 teacher head, 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicElectronic Packaging and Soldering TechnologiesFrench-language works237,207