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Record W2115924424 · doi:10.1109/ccece.2008.4564567

Steady state thermal analysis of a reconfigurable wafer-scale circuit board

2008· article· en· W2115924424 on OpenAlexafffundvenue
Mohammed Bougataya, A. Lakhsasi, Richard Norman, Richard Prytula, Yves Blaquière, Yvon Savaria

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsPolytechnique MontréalUniversité du Québec à MontréalUniversité du Québec en OutaouaisUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec en OutaouaisCMC Microsystems
KeywordsOverheating (electricity)ThermalFinite element methodThermal analysisPrinted circuit boardMechanical engineeringSolderingWaferMaterials scienceComputer scienceElectronic engineeringEngineeringElectrical engineeringStructural engineeringOptoelectronicsComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

During the development of a reconfigurable wafer-scale circuit board, the thermal design aspects have proved crucial to its reliable operation. Reducing thermally induced stress and preventing local overheating remain major concerns when optimizing the capabilities of the WaferBoardTMtechnology. This paper presents a thermal analysis of that technology. For this study, various thermal boundary conditions are analyzed and thermal profiles with 3D thermal contours are presented. 3D finite element thermal models are used to predict local thermal peaks on the WaferBoardTM structure. This model allows exploring the possibilities to minimize the thermal gradient in the critical areas, especially at the solder balls level. In a second step, thermal stress analysis will be conducted using the temperature loads calculated by steady state thermal analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.176
Teacher spread0.159 · 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 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

Citations4
Published2008
Admission routes3
Has abstractyes

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