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Large-Area Homoepitaxial Growth of Low-Doped Thick Epilayers for Power Devices with V<sub>BR</sub> > 4 kV

2009· article· en· W2030420874 on OpenAlexaff
Bernd Thomas, Christian Hecht, Birgit Kallinger

Bibliographic record

VenueMaterials science forum · 2009
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceWaferDopingEpitaxyEtching (microfabrication)OptoelectronicsLayer (electronics)DislocationNanotechnologyComposite material

Abstract

fetched live from OpenAlex

In this paper we present results on the growth of low-doped thick epitaxial layers on 4° off-oriented 4H-SiC using a commercially available hot-wall multi-wafer CVD system. For the first time we show results of a low-doped full-loaded 73” run on 4° off-oriented substrates with a layer thickness of more than 70 µm. The target doping concentration of 1.2×1015 cm-3 is suitable for blocking voltages > 6 kV. Results on doping, thickness and wafer-to-wafer homogeneities are shown. The surface quality of the grown layers was characterized by AFM. The density of different types of dislocations was determined by Defect Selective Etching.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.217
Teacher spread0.208 · 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.

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

Citations12
Published2009
Admission routes1
Has abstractyes

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