MétaCan
Menu
Back to cohort
Record W2120196988 · doi:10.1139/p09-029

Raman investigation of hydrogen-implanted and DC hydrogen-plasma-treated Cz Si wafers

2009· article· en· W2120196988 on OpenAlexvenueno aff
A. Saad

Bibliographic record

VenueCanadian Journal of Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
FundersBelarusian State University
KeywordsHydrogenWaferRaman spectroscopyIon implantationGetterAnalytical Chemistry (journal)Materials sciencePlasmaSiliconIonVacancy defectOptoelectronicsCrystallographyChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

The general goal of this work is to demonstrate that the buried defect layer created by hydrogen implantation can serve as a gettering region for hydrogen introduced from a DC plasma and to study the efficiency of this gettering affected by the implantation regimes. Standard n-type 4.5 Ω⋅cm Cz Si wafers were implanted by hydrogen ions with an energy of 100 keV and different doses of 1 × 10 14 , 1 × 10 15 , or 5 × 10 15 atoms/cm 2 at temperatures of 150, 300, 400, or 500 °C. After implantation, hydrogen was introduced to the wafers from the DC plasma at 150 °C. For a comparative estimation of the hydrogen concentration in the wafers implanted in different regimes Raman spectroscopy was used. The peaks of the Raman spectra associated with molecular hydrogen (H 2 ), vacancy-hydrogen (V–H), and silicon–hydrogen (Si–H) complexes were studied depending on the implantation conditions. It is demonstrated that peaks of Raman spectra depend significantly on the dose and temperature of implantation. This means that the concentration of hydrogen in the wafers could be determined from the concentration and type of defects formed by hydrogen implantation. Maximum peaks associated with H 2 , Si–H, and V–H complexes were observed for the samples implanted at a temperature of 500 °C.

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 score0.110
Threshold uncertainty score0.462

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 communication0.0000.000
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 head0.011
GPT teacher head0.184
Teacher spread0.172 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicSilicon and Solar Cell TechnologiesFrench-language works237,207