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Record W1995349408 · doi:10.1109/3.910453

The effects of InP grown by He-plasma assisted epitaxy on quantum-well intermixing

2001· article· en· W1995349408 on OpenAlexafffund
Tao Yin, G. Letal, B. J. Robinson, Dylan Thompson

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsQuantum wellPhotoluminescenceMaterials scienceOptoelectronicsMolecular beam epitaxyAnnealing (glass)EpitaxyIndium phosphideLayer (electronics)BlueshiftGallium arsenidePlasmaLaserOpticsNanotechnology

Abstract

fetched live from OpenAlex

He-plasma assisted InP (He*-InP) layers grown by gas source molecular beam epitaxy (GSMBE) have been employed to enhance quantum well (QW) intermixing induced by rapid thermal annealing in a 1.5 /spl mu/m InGaAsP QW laser structure. Inserting a 40 nm He*-InP layer just above the active region enhances the blue-shift for anneal temperatures larger than 680/spl deg/C, and a 42 nm additional blue-shift is obtained at 750/spl deg/C for samples with the He*InP layer, compared to samples with normal InP replacing the He*-InP. This is accompanied by a reduction in the photoluminescence (PL) intensity for anneal temperatures greater than 600/spl deg/C and is attributed to the migration of nonradiative defects from the He*-InP layer into the QWs. Insertion of a thin InGaAs layer between the He*-InP layer and the QW blocks the diffusion of these nonradiative defects into the QW. The results indicate that the He*-InP material could prove useful in QW intermixing to achieve integrated optoelectronic devices, in particular for high-frequency devices which require short carrier lifetimes.

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 score0.056
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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

Citations2
Published2001
Admission routes2
Has abstractyes

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