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Record W4309875114 · doi:10.1016/j.rinp.2022.106127

Study on the damage evolution of 6H-SiC under different phosphorus ion implantation conditions and annealing temperatures

2022· article· en· W4309875114 on OpenAlexaff
Jin-Jun Gu, Jin-Hua Zhao, Ming-Yang Bu, Sumei Wang, Fan Li, Qing Huang, Shuang Li, Qing-Yang Yue, Xuelin Wang, Zhi‐Xian Wei, Yong Liu

Bibliographic record

VenueResults in Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsInstitute of Particle Physics
FundersNatural Science Foundation of Shandong ProvinceState Key Laboratory of Nuclear Physics and Technology, Peking UniversityChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsAnnealing (glass)Ion implantationMaterials scienceIonPhosphorusAnalytical Chemistry (journal)MetallurgyOptoelectronicsChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

In this work, we investigate the radiation damage of 6H-SiC crystals by phosphorus (P) ion implantation. The 6H-SiC samples were implanted at different energy with the same fluence of 5.0 × 10 14 ions/cm 2 at room temperature, one of which was subsequently annealed. Raman and absorption spectra were obtained to probe the structure and optical properties of 6H-SiC crystals after P ion implantation. In addition, Rutherford backscattering/channeling spectroscopy and transmission electron microscopy were used to explore further the damage behavior at different implantation energy and damage evolution with post-annealing treatment. It was found that the damage of the 200 keV implanted sample partially recovered after annealing at 600 °C for 60 min and disappeared at an annealing temperature of 800 °C. The damage evolution behavior of phosphorus ions implantation into 6H-SiC crystals with different implantation and annealing conditions is presented in our work.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

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