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Thermal Management of GaN Power Modules Using Copper Core PCBs with Direct Heatsink Pads

2023· article· en· W4390957556 on OpenAlexaff
Jingyuan Liang, Qi Liu, Wentao Cui, Wai Tung Ng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHeat sinkMaterials scienceGallium nitridePower modulePower densityTransistorOptoelectronicsCapacitorPower semiconductor deviceThermal resistanceInductorBuck converterPower (physics)Electrical engineeringThermalEngineeringPhysicsNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

With the adoption of wide-bandgap (WBG) semiconductor devices in high power density applications, the thermal dissipation of this type of power modules has become a major challenge. In the power conversion applications with Gallium Nitride (GaN) high electron mobility transistors (HEMTs), the switching frequency is normally a few times higher than those using similar rated silicon devices. The increase in frequency can reduce the system's physical size and achieve a more compact design. However, the compactness of the module poses greater challenges on system's thermal design as the effective area for heat dissipation is reduced. A GaN based, buck converter module using copper core PCB with direct heatsink pad is designed and tested to demonstrate one possible methods of mitigating this issue. This module integrates input and output capacitors, four GaN transistors, four gate drivers and a coupled inductor to construct a two-phase buck converter that converts 12 to 5 V or 3.3 V. When operated at 500 kHz for a 12 to 5 V conversion with forced air cooling and additional heatsinks, this module can achieve a peak efficiency of 92.5% at 25 W of output power. It can deliver a maximum power of 176 W while maintaining the temperature of the GaN transistors to be below 90°C. At this maximum power, the power density is calculated to be 9.8 kW/L.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.516

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 head0.032
GPT teacher head0.243
Teacher spread0.211 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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