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Record W2936669955 · doi:10.1116/1.5089919

On the optimum off-cut angle for the growth on InP(111)B substrates by molecular beam epitaxy

2019· article· en· W2936669955 on OpenAlexafffund
Ida Sadeghi, Man Chun Tam, Z. R. Wasilewski

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooOntario Centres of Excellence
KeywordsHillockMaterials scienceVicinalMolecular beam epitaxyDifferential interference contrast microscopyEpitaxyMisorientationOptoelectronicsWaferMicroscopyOpticsNanotechnologyComposite materialLayer (electronics)ChemistryMicrostructure

Abstract

fetched live from OpenAlex

InGaAs and InAlAs epilayers were grown on InP(111)B substrates by molecular beam epitaxy. Rather than focusing on a specific off-cut angle, the growths were done on rounded wafer edges, which expose a broad spectrum of vicinal surfaces with varying off-cut angle and off-cut azimuth. The epilayers were grown at several different growth conditions by varying the growth temperature, growth rate, and arsenic (As) overpressure. The epitaxial layers were characterized at the center and the edge of the wafers using Nomarski differential interference contrast microscopy and atomic force microscopy. It was shown that a minimum misorientation angle of ∼0.4° should be used in order to avoid pyramidal hillocks. At higher misorientations, 1.7°–3°, step bunching can lead to surface roughening.

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)0.002
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 score0.029
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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