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Record W2048924636 · doi:10.1116/1.2201051

Influence of layer thickness and compositional variations on the electrorefractive properties of a quantum well polarization-conversion modulator

2006· article· en· W2048924636 on OpenAlexafffund
Sasa Ristic, Nicolas A. F. Jaeger

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsElectric fieldPolarization (electrochemistry)Materials scienceChirpRefractive indexQuantumQuantum wellOptical modulatorOptoelectronicsOpticsPhysicsChemistryPhase modulationQuantum mechanics

Abstract

fetched live from OpenAlex

We present our work done towards the development of a quantum well polarization-conversion modulator. This modulator will consist of multiple repetitions of quantum well structures exhibiting large electrorefractive effects. In them, a channel waveguide supporting only the fundamental TE-like and TM-like modes will be subjected to an applied electric field. Changes in this applied field cause the effective refractive index of one of the modes to increase and that of the other to decrease. Using these structures shows that short (∼2–3mm) modulators, with 90° polarization rotation, low chirp (∼±0.1), and low drive electric fields (∼10kV∕cm) can be achieved. In this article, numerical simulations based on the effective-mass envelope-function approximation are used to study the influence of layer thickness and compositional variations on the electrorefractive properties of these quantum well structures.

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 score0.151
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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