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Record W2998069968 · doi:10.14738/tnc.76.7569

Study of Miniaturization of a Silicon Vapor Chamber for Compact 3D Microelectronics, via a Hybrid Analytical and Finite Element Method

2019· article· en· W2998069968 on OpenAlexaff
Yue Ma, Mahmood R. S. Shirazy, Quentin Struss, P. Coudrain, J. P. Colonna, A. Souifi, Luc G. Fréchette, Christian Gontrand

Bibliographic record

VenueTransactions on Networks and Communications · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMiniaturizationMicroelectronicsSiliconMaterials scienceParametric statisticsFinite element methodThermal conductivityOptoelectronicsMechanical engineeringNanotechnologyComposite materialEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The interest in silicon vapor chambers (SVCs) has increased in the recent years as they have been identified as efficient cooling systems for microelectronics. They present the advantage of higher thermal conductivity and smaller form factor compared to conventional heat spreaders. This work aims to investigate the potential miniaturization of these devices, preliminary to integration on the backside of mobile device chips, located as close as possible to hotspots. While detailed numerical models of vapor chamber operation are developed, an easy modeling with low computational cost is needed for an effective parametric study. Based on the study of the operating limits, this paper shows the thinning potential of a water filled micropillar for a device operating below 10 W and identify the corresponding vapour core height, and wick thickness.

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: none
Teacher disagreement score0.922
Threshold uncertainty score0.436

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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