MétaCan
Menu
Back to cohort
Record W2031958080 · doi:10.1088/0268-1242/15/12/101

Enhanced bandgap blue-shift in InGaAsP multiple-quantum-well laser structures by low-temperature-grown InP

2000· article· en· W2031958080 on OpenAlexaff
A S W Lee, D. A. Thompson, B. J. Robinson

Bibliographic record

VenueSemiconductor Science and Technology · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBlueshiftPhotoluminescenceOptoelectronicsAnnealing (glass)EpitaxyQuantum wellMolecular beam epitaxyBand gapMaterials scienceLaserDielectricIndium gallium arsenideIndium phosphideGallium arsenideChemistryOpticsLayer (electronics)Nanotechnology

Abstract

fetched live from OpenAlex

Quantum well intermixing (QWI) in an InGaAsP multiple-quantum-well (MQW) laser structure is demonstrated using an InP epitaxial layer grown at 300 °C, by gas source molecular beam epitaxy, followed by rapid thermal annealing. Photoluminescence is used to compare the magnitude of the QWI process between low-temperature (LT)- and normal-temperature (NT, 470 °C)-grown InP layers as a function of both anneal temperature and time. For example, after an anneal at 780 °C, a large bandgap blue-shift of ~197 nm is observed in MQW structures capped with LT-InP as compared to an ~35 nm shift in identical structures capped with NT-InP. Also, the effect of the LT-InP capping is compared to NT-InP, capped with a dielectric (~100 nm of SiO 2 ), following anneal at 800 °C for 60 s. This shows blue-shifts of ~243 and ~142 nm, respectively.

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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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

Citations11
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueSemiconductor Science and TechnologySame topicSemiconductor Quantum Structures and DevicesFrench-language works237,207