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Influence of Substrate Preparation and Epitaxial Growth Parameters on the Dislocation Densities in 4H-SiC Epitaxial Layers

2008· article· en· W2054867286 on OpenAlexaff
Birgit Kallinger, Bernd Thomas, Jochen Friedrich

Bibliographic record

VenueMaterials science forum · 2008
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsInfineon Technologies (Canada)
FundersBayerische Forschungsstiftung
KeywordsEpitaxyMaterials scienceDislocationSubstrate (aquarium)OptoelectronicsLayer (electronics)Etching (microfabrication)Basal planeCrystallographyComposite materialChemistry

Abstract

fetched live from OpenAlex

Basal Plane Dislocations (BPD) in SiC are thought to cause degradation of bipolar devices as they can trigger the formation and expansion of stacking faults during device operation. Therefore, epilayers without any BPD are strongly recommended for the achievement of long-term reliable bipolar devices. Such epilayers can be achieved by supporting the conversion of BPD into Threading Dislocations (TD), which depends on the epitaxial growth mode (as described in literature). In this work, the influence of several pre-treatments of the SiC substrate prior to epitaxial growth and different epitaxial growth parameters on the reduction of the BPDs in the SiC epilayers was investigated on 4° off-axis substrates. The dislocation content in substrates and epilayers was determined by Defect Selective Etching (DSE) in molten KOH. The averaged BPD density in epitaxial layers can be reduced to < 100 cm-2 for substrate preparation techniques and to < 30 cm-2 for well-suited epitaxial growth parameters. A certain combination of epitaxial growth parameters leads to < 3 BPD/cm2 in the epitaxial layer.

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 score0.067
Threshold uncertainty score0.380

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 studies0.0000.001
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 head0.015
GPT teacher head0.219
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

Citations13
Published2008
Admission routes1
Has abstractyes

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