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Record W2925080256 · doi:10.1116/1.5088974

Unintentional As incorporation into AlSb and interfacial layers within InAs/AlSb superlattices

2019· article· en· W2925080256 on OpenAlexafffund
Yunong Hu, 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
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceShutterSuperlatticeMolecular beam epitaxyFlux (metallurgy)WaferOptoelectronicsEpitaxyDiffractionQuantum wellTernary operationLaserNanotechnologyOpticsLayer (electronics)

Abstract

fetched live from OpenAlex

The InAs/AlSb material system has proven to be an excellent choice for high-performance mid-infrared quantum cascade lasers. In this work, an unintentional displacement of Sb by residual As flux incident on the wafer was studied by direct monitoring of such flux during a simulated molecular beam epitaxy (MBE) growth sequence and dynamical simulations of high-resolution x-ray diffraction data collected on InAs/AlSb periodic structures grown under similar conditions. The results revealed that predominantly Al–As bonds detected at the InAs/AlSb interfaces, which were reported earlier, can be attributed to a residual bypass As flux on the wafer after closing the As shutter. Moreover, the experiments revealed that under typical growth conditions, AlSb binary barriers are converted into AlAsSb ternary layers with appreciable As content. The exact As content in the barriers is proportional to the effective As flux bypassing the closed shutter and thus depends on the particulars of the MBE system design and the exact As flux used for the growth of InAs wells.

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)0.001
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 score0.036
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.239
Teacher spread0.231 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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