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Record W4200527933 · doi:10.1002/eng2.12489

Comparison of the parasitic impedances from the drain‐source path of power transistor packages at up to 2 GHz

2021· article· en· W4200527933 on OpenAlexaff
Thomas Moldaschl, Stefan Woetzel, Riccardo Latella, Maurizio Galvano, Alfred Binder

Bibliographic record

VenueEngineering Reports · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInfineon Technologies (Canada)
FundersHorizon 2020 Framework ProgrammeElectronic Components and Systems for European LeadershipEuropean Commission
KeywordsParasitic elementHigh-electron-mobility transistorParasitic extractionTransistorElectrical impedanceInductancePower (physics)OptoelectronicsMaterials scienceElectronic engineeringElectrical engineeringComputer sciencePhysicsEngineeringVoltage

Abstract

fetched live from OpenAlex

Abstract In this article, we compare several concepts for gallium‐nitride (GaN) power high electron mobility transistor (HEMT) packages in terms of their drain‐to‐source parasitic inductance and resistance values. Unlike in previous studies, however, due to several fast transient use cases the investigated frequencies are extended to 2 GHz. Since the device's dimensions are already a significant fraction of the corresponding wavelength at 2 GHz, full wave simulation tools have been employed to more accurately assess the problem. The results reveal how the considered package concepts affect the overall parasitic device impedances and show a clear improvement of the parasitic impedance over each generation of package implementation. Furthermore, full wave analysis results are compared to method of moments results to emphasize the necessity of full wave simulations at these frequencies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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