MétaCan
Menu
Back to cohort

Characterization of Near-Interface Traps at Dielectric/SiC Interfaces Using CCDLTS

2019· article· en· W2964102236 on OpenAlexfundno aff
Isanka Udayani Jayawardhena, Asanka Jayawardena, Chun Kun Jiao, Dallas Morisette, Sarit Dhar

Bibliographic record

VenueMaterials science forum · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
FundersSimon Fraser UniversityAuburn University
KeywordsMaterials sciencePassivationDielectricDeep-level transient spectroscopySilicon carbideOxideTrappingHigh-κ dielectricSiliconAnalytical Chemistry (journal)Molecular physicsOptoelectronicsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Charge trapping at 4H-SiC/dielectric interfaces in 4H-SiC MOS capacitors has been investigated using constant capacitance deep level transient spectroscopy (CCDLTS). The experiments were focused on further understanding of the following aspects related to 4H-SiC/SiO 2 interfaces: (i) Origin of near interface oxide traps (NITs), (ii) Effect of interfacial impurity/passivation methods and (iii) Characterization of near-interface oxide traps for different SiC wafer orientations. For the (0001) Si-face 4H-SiC/ SiO 2 interface, two types of NITs are typically detected by CCDLTS, named ‘O1’ and ‘O2’ traps with emission activation energies of about 0.15±0.05 eV and 0.39±0.1 eV respectively below the 4H-SiC conduction band. Based on comparison with previous ab initio calculations, the physical identities of these defects have been suggested to be carbon dimers substituted for O dimers (‘O1’) and interstitial silicon atoms (‘O2’) in the near interfacial SiO 2 respectively. In this work, it is shown for the first time that such traps are not observed for 4H-SiC/ Al 2 O 3 interfaces, proving that these traps are inherent to the near-interfacial SiO 2 . In addition, the summary of CCDLTS results for Si-face with different interface trap passivation methods are included in this study. Finally, a comparison is presented for NO annealed (0001) Si-face, (11-20) a-face and (000-1) C-face interfaces that highlight the difference of CCDLTS signatures for the different crystal faces.

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 score0.014
Threshold uncertainty score0.600

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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207