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Analysis on the Effect of Laval Microchannel Structure in Si Interposer for GaN HEMTs Cooling

2020· article· en· W3088738701 on OpenAlexaboutno aff
Miao Yu, Jian Zhu, Min Huang, Hongze Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMicrochannelMicrosystemMaterials scienceOptoelectronicsThermal resistanceWater coolingJunction temperatureGallium nitrideInterposerElectronic engineeringThermalHeat transferMechanical engineeringMechanicsEngineeringNanotechnologyEtching (microfabrication)Layer (electronics)PhysicsThermodynamics

Abstract

fetched live from OpenAlex

The microfluid cooling is widely used in the thermal management for high power integration in 3D Si RF microsystem. The simple and efficient microfluid structure is essential in large area cooling for the module with multiple active devices integrated on silicon. A novel Laval microchannel structure was proposed in this paper, and it was simple to design and easy for process. The thermal and hydraulic characteristics of Laval microchannel were investigated and compared with the conventional straight microchannel and 2 kinds of common serpentine microchannels using FEM analysis. The results indicated that Laval microchannel had low flow resistance to maintain high pumping power and achieved the best cooling performance among different microchannel structures. And Laval structure can be effective to keep junction temperature below 150 °C which can provide the sufficient condition for GaN HEMTs device operating with high efficiency and good linearity. It presented remarkable cooling performance for GaN HEMTs cooling integrated on Si, showing excellent candidate structure for large area cooling in 3D Si RF microsystem.

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 score0.292
Threshold uncertainty score0.178

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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