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Record W3163051946 · doi:10.4236/jectc.2021.102002

Best Practices for Thermal Modeling in Microelectronics with Natural Convection Cooling: Sensitivity Analysis

2021· article· en· W3163051946 on OpenAlexafffund
Mamadou Kabirou Touré, Papa Momar Souaré, Julien Sylvestre

Bibliographic record

VenueJournal of Electronics Cooling and Thermal Control · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaInternational Business Machines Corporation
KeywordsMicroelectronicsNatural convectionHeat transferConvectionThermalMaterials scienceHeat transfer coefficientForced convectionMechanicsThermal conductivitySensitivity (control systems)Convective heat transferMechanical engineeringMeteorologyEngineeringElectronic engineeringPhysicsComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

A detailed sensitivity study was carried out on various key parameters from a high precision numerical model of a microelectronic package cooled by natural convection, to provide rules for the thermal modeling of microelectronic packages subjected to natural convection heat transfer. An accurate estimate of the junction temperature, with an error of less than 1˚C, was obtained compared to the experimental data for the vertical and horizontal orientations of the test vehicle in the JEDEC Still Air configuration. The sensitivity study showed that to have an accurate estimate of the temperature, the following elements should be present in the thermal model: radiation heat transfer in natural convection cooling; a computational fluid dynamics analysis to find realistic convection coefficients; detailed models of the high conductivity elements in the direction of the heat flow towards the environment; and finally precise values for the thicknesses of layers and the thermal properties of the substrate and the printed circuit board.

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)0.001
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 score0.316
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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