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Record W2017971570 · doi:10.1063/1.2033143

Ion channeling effects on quantum well intermixing in phosphorus-implanted InGaAsP∕InGaAs∕InP

2005· article· en· W2017971570 on OpenAlexafffund
David Barba, B. Salem, Denis Morris, Vincent Aimez, Jacques Beauvais, M. Chicoine, F. Schiettekatte

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversité de MontréalUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsBlueshiftPhotoluminescenceHeterojunctionRaman spectroscopyMaterials scienceIonAnnealing (glass)Ion implantationQuantum wellOptoelectronicsBand gapMolecular physicsChemistryOpticsLaserPhysics

Abstract

fetched live from OpenAlex

Photoluminescence, time-resolved photoluminescence, and Raman characterization techniques have been used to study In0.73Ga0.27As0.57P0.43∕In0.53Ga0.47As∕InP single quantum well heterostructure after 20-keV phosphorus ion implantation followed by rapid thermal annealing. The annealing process induces intermixing in the heterostructures and results in the blueshift of the quantum well peak emission. In order to investigate ion channeling effects on this band-gap tuning process, room-temperature implantations have been performed at tilt angles of 0° and 7° with respect to the sample (001)-growth axis. We show that the ion channeling increases the blueshift from 24 to 42 nm, while it reduces both the density of the nonradiative defects within the active layer and the structure disordering. These features are attributed to the nature of the damage generated by channeled ions. The band-gap increase observed in the sample implanted at 0° is consistent with the formation of a compressive strain at the barrier/quantum well interface, whose intensity is measured by Raman spectroscopy.

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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

Citations6
Published2005
Admission routes2
Has abstractyes

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