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Record W4296987589 · doi:10.1364/oe.470615

High-responsivity operation of quantum cascade detectors at 9 µm

2022· article· en· W4296987589 on OpenAlexaff
Georg Marschick, Mauro David, Elena Arigliani, Nikola Opačak, Benedikt Schwarz, Miriam Giparakis, Alexandre Delga, M. Lagrée, Thomas Poletti, Virginie Trinité, Axel Evirgen, Bruno Gérard, Georg Ramer, Richard Maulini, Jérémy Butet, Stéphane Blaser, A. M. Andrews, G. Strasser, Borislav Hinkov

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsNokia (Canada)
FundersHorizon 2020 Framework ProgrammeÖsterreichische ForschungsförderungsgesellschaftAir Force Office of Scientific ResearchAustrian Science Fund
KeywordsResponsivityOpticsDetectorCascadeHeterodyne detectionOptoelectronicsMaterials scienceInfraredWavelengthOptical communicationDark currentPhysicsLaser

Abstract

fetched live from OpenAlex

Quantum cascade detectors (QCDs) are devices operating at zero external bias with a low dark-current. They show linear detection and high saturation intensities, making them suitable candidates for heterodyne detection in long-wave infrared (LWIR) free space optical communication systems. We present an approach to mitigate the performance limitation at long wavelengths, by a comparison of similar single and multi-period QCDs for optimizing their responsivity and noise behaviour. Our InGaAs/InAlAs/InP ridge QCDs are designed for operation at λ = 9.124 µm. Optical waveguide simulations support the accurate optical characterization. A detailed device analysis reveals room-temperature responsivities of 111 mA/W for the 15-period and 411 mA/W for the single-period device.

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 categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.998

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)0.0020.000

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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