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Record W2612826142 · doi:10.1109/eurosime.2017.7926259

Highly parallel computations of creep deformation in flip-chip interconnections

2017· preprint· en· W2612826142 on OpenAlexaff
Cedrick Bouchard, Julien Sylvestre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCreepInterconnectionFlip chipComputationComputer scienceDeformation (meteorology)Stress (linguistics)Temperature cyclingChipThermalParallel computingMaterials scienceStructural engineeringAlgorithmComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

In order to enable the computation of creep deformation in a large number of interconnections in simulations of thermal cycling or assembly processes of flip chip devices, a submodeling approach was developed to distribute the stress and creep evaluation to independent solvers for individual (or small number of) interconnections and thus allow a high level of parallelization of the solving effort. The approach uses a first, coarser model of the complete assembly to compute the general system response to thermal and mechanical loads. Displacements calculated in this first model are fed into a large number of much more detailed models of the individual interconnections, in order to obtain local creep strains. The coarser model is iteratively updated using local creep values from the interconnection models to determine the complete time evolution of the system in the presence of interconnection creep deformation. The validity of the complete parallelized procedure is verified on simplified cases.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.714

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 integrity0.0000.001
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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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