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Record W4296365900 · doi:10.1063/5.0102644

Dual role of 3C-SiC interlayer on DC and RF isolation of GaN/Si-based devices

2022· article· en· W4296365900 on OpenAlexaff
A. El Hadi Khediri, B. Benbakhti, Jean-Claude Gerbedoen, Hassan Maher, Abdelatif Jaouad, N. Bourzgui, A. Soltani

Bibliographic record

VenueApplied Physics Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsMaterials scienceSilicon carbideOptoelectronicsRaman spectroscopyLayer (electronics)Wide-bandgap semiconductorBreakdown voltageNucleationSubstrate (aquarium)TransistorSiliconElectron mobilityVoltageNanotechnologyElectrical engineeringOpticsComposite materialChemistry

Abstract

fetched live from OpenAlex

The impact of Cubic Silicon Carbide (3C-SiC) transition layer on breakdown voltage and frequency performance of GaN high electron mobility transistors is investigated. A combination of distinct material and device characterizations techniques, including Raman spectroscopy, coplanar waveguides, electrical measurements, and Technology Computer-Aided Design (TCAD) simulations, are adopted to inspect the role of the 3C-SiC interlayer. Raman spectra reveal a good quality of the 3C-SiC layer, similar to the mono-crystalline 3C-SiC spectra. A relatively low transmission loss of ∼0.16 dB/mm at 40 GHz is measured for the device with 3C-SiC layer, rather than 2.1 dB/mm for the device without 3C-SiC. In addition, a soft breakdown voltage around 1530 V at 1 μA/mm is achieved, which is three times larger compared with that of the conventional device. The failure mechanism, related to carrier injection at the nucleation layer, is not observed in the structure with the 3C-SiC layer. Instead, TCAD simulations disclose a substantial improvement of the buffer/substrate interface through the suppression of an interface current path.

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 score0.011
Threshold uncertainty score0.661

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics LettersSame topicGaN-based semiconductor devices and materialsFrench-language works237,207