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Record W3106916563 · doi:10.1149/09702.0043ecst

(Invited) Effect of the Plasma Etching on InAsP/InP Quantum Well Structures Measured through Low Temperature Micro-Photoluminescence and Cathodoluminescence

2020· article· en· W3106916563 on OpenAlexaff
Jean-Pierre Landesman, Nebile Işık Göktaş, Ray LaPierre, Shahram Ghanad-Tavakoli, E. Pargon, Camille Petit-Étienne, Christophe Levallois, J. Jiménez, Shabnam Dadgostar

Bibliographic record

VenueECS Transactions · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCathodoluminescencePhotoluminescenceEtching (microfabrication)Quantum wellLuminescenceMaterials sciencePlasmaAnalytical Chemistry (journal)OptoelectronicsPlasma etchingChemistryOpticsLaserNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Photoluminescence and cathodoluminescence spectral imaging were performed across rectangular stripes etched in samples with InAs x P 1-x quantum wells of constant thickness and variable composition grown on InP. In particular, the effects of different etching chemistries (CH 4 /H 2 /Ar and Cl 2 /CH 4 /Ar) were investigated. The results discussed deal with modifications of the luminescence line shapes (which differ with etching process) and with the intensity variation of the emissions associated with the quantum wells across the stripes. The possible origins of these effects are investigated in terms of carrier recombination on the vertical sidewalls of the stripes and lateral diffusion of species from the plasma during etching. Cathodoluminescence measurements on samples under DC-bias also show the quantum confined Stark effect which is correlated to the material modifications induced by the etching.

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 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 score0.863

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 integrity0.0000.001
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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