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Record W4321488125 · doi:10.1109/jqe.2023.3246987

Tunable Hybrid Plasmonic Semiconductor Laser Based on Loss Perturbation

2023· article· en· W4321488125 on OpenAlexafffund
Shayan Saeidi, Pierre Berini

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLasing thresholdOptoelectronicsPlasmonLaserSemiconductor laser theoryCapacitorSemiconductorIndium tin oxideOpticsWavelengthPhysicsVoltageNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

We propose a tunable plasmonic semiconductor laser that exploits loss perturbation as a tuning mechanism. A metal oxide semiconductor (MOS) capacitive structure is added on top of an edge-emitting Fabry-Perot (FP) diode laser, such that a hybrid plasmonic TM mode that overlaps partly with the MOS capacitor and the semiconductor gain region is supported as the lasing mode. We also propose the use of a layer of conductive oxide, e.g., indium tin oxide (ITO), as the semiconductor of the MOS structure, because the epsilon near zero (ENZ) condition can be attained therein under accumulation, thereby producing a very large change in the effective index of the hybrid plasmonic TM mode. The change in the imaginary part of the effective index is used to tune the lasing wavelength - exploiting loss perturbation to achieve laser tuning is paradigm-shifting. The laser proposed operates at telecom wavelengths, requiring an electrical forward bias to pump the active layer, and a gate voltage to drive the MOS tuning capacitor. Simulations yield a tuning range of over 7 nm in the O-band for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 \mu \text{m}$ </tex-math></inline-formula> long FP laser cavity.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes2
Has abstractyes

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