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Record W2336169177 · doi:10.1088/0268-1242/31/6/065007

Defect energy levels in p-type GaAsBi and GaAs grown by MBE at low temperatures

2016· article· en· W2336169177 on OpenAlexafffund
P. M. Mooney, Marianne C. Tarun, Vahid Bahrami-Yekta, T. Tiedje, Ryan B. Lewis, Mostafa Masnadi‐Shirazi

Bibliographic record

VenueSemiconductor Science and Technology · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of British ColumbiaUniversity of VictoriaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnergy (signal processing)Materials scienceChemistryOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract Deep level defects in p-type GaAs 1− x Bi x ( x < 1%) and GaAs grown by molecular beam epitaxy at substrate temperatures of 330 °C and 370 °C have been characterized by deep level transient spectroscopy. We find that incorporating Bi into GaAs at 330 °C does not affect the total concentration of hole traps, which is ∼4 × 10 16 cm −3 , comparable to the concentration of electron traps observed in Si-doped GaAsBi having a similar alloy composition. Increasing the growth temperature of the p-type GaAsBi ( x = 0.8%) layer from 330 °C to 370 °C reduces the hole trap concentration by an order of magnitude. Moreover, the defects having near mid-gap energy levels that are the most efficient non-radiative recombination centers are present only in GaAsBi layers grown at the lower temperature. These new results are discussed in the context of previous measurements of n-type GaAs and GaAsBi layers grown under similar conditions.

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 score0.004
Threshold uncertainty score0.008

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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