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Record W2148034507 · doi:10.1109/tdmr.2008.916302

High-Temperature Very Low Frequency Noise-Based Investigation of Slow Transients in AlGaN/GaN MODFETs

2008· article· en· W2148034507 on OpenAlexaff
Pouya Valizadeh

Bibliographic record

VenueIEEE Transactions on Device and Materials Reliability · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceNoise (video)OptoelectronicsAtmospheric temperature rangeInfrasoundTransistorWide-bandgap semiconductorDopingModulation (music)Field-effect transistorPhysicsVoltageAcoustics

Abstract

fetched live from OpenAlex

The variations of the very low frequency noise (i.e., 100 mHz to 100 kHz) and the dc characteristics of unpassivated AlGaN/GaN modulation-doped field-effect transistors (MODFETs) with temperature from 300 to 500 K are investigated. The rise in temperature to 500 K is shown to reveal generation-recombination (G-R) noise characteristics within the 100 mHz to 1 Hz frequency range. It is experimentally evidenced that these manifestations can predict the existence of very slow transients in the drain-current characteristics of AlGaN/GaN MODFETs. Due to the very long time constant of the transients at room temperature (i.e., on the order of several hours or even days), the observed features can be classified as semipermanent. The comparison between the energy levels predicted by the noise data and the levels previously reported in literature supports these observations.

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)0.000
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.218
Teacher spread0.206 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Device and Materials ReliabilitySame topicGaN-based semiconductor devices and materialsFrench-language works237,207