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Record W1983565110 · doi:10.1002/pssa.200881285

Electroluminescence in plasma ion implanted silicon

2009· article· en· W1983565110 on OpenAlexaff
P Desautels, Michael P. Bradley, J. T. Steenkamp, James Mantyka

Bibliographic record

Venuephysica status solidi (a) · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSiliconElectroluminescenceLuminescenceMaterials scienceIonAnnealing (glass)OptoelectronicsIon implantationPlasmaPhotoluminescenceAnalytical Chemistry (journal)NanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Electroluminescent silicon is very desirable for its potential applications in computing and telecommunications. Plasma Ion Implantation (PII) is a means to modify the surface and sub‐surface properties of silicon to produce luminescent centres. Silicon to be treated with PII is immersed in low‐temperature plasma and biased to a high, negative voltage; this accelerates ions into the sample and implants them beneath its surface. The material is subsequently annealed in a furnace and fitted with thin gold and aluminium contacts. Samples of crystalline silicon were implanted with H ions, C ions, and N ions and found to emit visible light when subjected to electric current. Nearly every sample emitted light at ∼460 nm and ∼630 nm; these luminescence bands are commonly observed in X‐ray excited optical luminescence studies of silicon nanostructures and have been attributed to silicon dioxide (SiO 2 ) and the silicon‐SiO 2 interface, respectively [1]. Other luminescent bands may be the product of nanostructures and defects created by the implantation process and subsequent annealing. This presentation will explore in detail the results of these experiments and the potential of PII for modifying the luminescent properties of elemental silicon. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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 score0.047
Threshold uncertainty score0.995

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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