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Record W2085889460 · doi:10.1063/1.2969063

ArF laser-based quantum well intermixing in InGaAs/InGaAsP heterostructures

2008· article· en· W2085889460 on OpenAlexaff
Jonathan Genest, Romain Béal, Vincent Aimez, Jan J. Dubowski

Bibliographic record

VenueApplied Physics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsOptoelectronicsHeterojunctionQuantum wellMaterials scienceWaferLaserFabricationAnnealing (glass)Gallium arsenideBand gapIrradiationOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Radiation from a 193 nm ArF laser was investigated to modify surface properties of InGaAs/InGaAsP quantum well (QW) heterostructures and introduce defects required to enhance intermixing during the annealing process. A top 200 nm thick sacrificial layer of InP served as a reservoir for laser generated defects. The irradiation with up to 90 pulses at 65–150 mJ/cm2 allowed to generate an array of 1.2×1 mm2 sites of QW intermixed material, with bandgap energy blueshifted up to 107 nm. We discuss the mechanism and advantages of this approach for postgrowth wafer level fabrication of multibandgap QW material.

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 categoriesMeta-epidemiology (narrow)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.198
Teacher spread0.184 · 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.

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

Citations18
Published2008
Admission routes1
Has abstractyes

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