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Record W2920789622 · doi:10.1109/eptc.2018.8654285

Estimation of Maximum Operating Temperature for Cu Wire Bonds: Comparison of Epoxy and Silicone Encapsulant Types

2018· article· en· W2920789622 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
KeywordsExtrapolationSiliconeMaterials scienceEpoxyMaximum temperatureComposite materialReliability (semiconductor)Junction temperatureArrhenius equationOperating temperatureWire bondingFailure rateThermodynamicsElectrical engineeringPower (physics)MathematicsChemistryActivation energyStatisticsPhysics

Abstract

fetched live from OpenAlex

To add to the knowledge of high-temperature reliability of Cu wire bonds, this work reports a method and data for the estimation of maximum operating temperature levels for various Cu wire and encapsulant combinations. An accelerated failure time model based on the Arrhenius equation was used to describe the dependence of the degradation times on the aging temperature and to recommend maximum operating temperature values. Failure was defined as +10 % resistance change. However, due to time constraints, insufficient failure time data was available for model fitting using this failure criterion. Instead, models were fit using a failure criterion of 1 % resistance increase, then extrapolated to the original failure criterion, assuming a linear rate of resistance increase. Using this extrapolation, it was estimated that to keep the failure probability below 1 ppm, the recommended maximum operating temperature levels for a standard lifetime of 12 kilohours with PCC wire are 163 °C, 145 °C, and 141 °C for encapsulants air, epoxy, and silicone, respectively. Corresponding maximum temperatures for bare Cu wire are 159 °C and 125 °C for epoxy and silicone, respectively. Bare Cu wire in air had poor reliability at 225 °C and so was not tested at 200 °C or 175 °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 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 score0.388
Threshold uncertainty score0.267

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 head0.013
GPT teacher head0.262
Teacher spread0.250 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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