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Record W4404229223 · doi:10.1063/5.0234657

Generating stacking faults in 4H–SiC junction transistor by indentation and forward biasing

2024· article· en· W4404229223 on OpenAlexafffund
Tingwei Zhang, Adrian Kitai

Bibliographic record

VenueAIP Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsBiasingStackingIndentationMaterials scienceTransistorOptoelectronicsSilicon carbideElectrical engineeringComposite materialVoltageEngineeringPhysicsNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Stacking faults in silicon carbide have been widely studied due to their negative impact on the application of silicon carbide in the power electronics industry. In this work, with the assistance of forward biasing, we observe several triangular shaped structures emerging near the indenter imprint in two separate 4H–SiC bipolar junction transistor samples that were deformed by nanoindentation. Based on the study of electroluminescence spectra on one of the samples, the emission peak at 420 nm indicates the formation of single Shockley stacking faults inside deformed transistors. We conclude that the use of indentation can provide a method to study recombination induced stacking faults in silicon carbide junction devices by intentionally introducing dislocations at selected areas of interest.

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 score0.421
Threshold uncertainty score0.470

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 communication0.0000.001
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 head0.242
Teacher spread0.233 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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