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Record W4323567481 · doi:10.1364/oe.487119

Monolithic high contrast grating on GaSb/AlAsSb based epitaxial structures for mid-infrared wavelength applications

2023· article· en· W4323567481 on OpenAlexaff
Anne Schade, Andreas Bader, Tobias Huber, Stefan Kühn, Tomasz Czyszanowski, Andreas Pfenning, Michał Rygała, Tristan Smółka, M. Motyka, G. Sęk, Fabian Hartmann, Sven Höfling

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversity of British Columbia
FundersNarodowa Agencja Wymiany AkademickiejNarodowe Centrum Nauki
KeywordsMaterials scienceOpticsOptoelectronicsDistributed Bragg reflectorGratingWavelengthInfraredPolarization (electrochemistry)Physics

Abstract

fetched live from OpenAlex

epitaxial structures with sub-wavelength gratings enabling high reflection of unpolarized mid-infrared radiation at the wavelength range from 2.5 to 5 µm. We study the reflectivity wavelength dependence of MHCGs with ridge widths ranging from 220 to 984 nm and fixed 2.6 µm grating period and demonstrate that peak reflectivity of above 0.7 can be shifted from 3.0 to 4.3 µm for ridge widths from 220 to 984 nm, respectively. Maximum reflectivity of up to 0.9 at 4 µm can be achieved. The experiments are in good agreement with numerical simulations, confirming high process flexibility in terms of peak reflectivity and wavelength selection. MHCGs have hitherto been regarded as mirrors enabling high reflection of selected light polarization. With this work, we show that thoughtfully designed MHCG yields high reflectivity for both orthogonal polarizations simultaneously. Our experiment demonstrates that MHCGs are promising candidates to replace conventional mirrors like distributed Bragg reflectors to realize resonator based optical and optoelectronic devices such as resonant cavity enhanced light emitting diodes and resonant cavity enhanced photodetectors in the mid-infrared spectral region, for which epitaxial growth of distributed Bragg reflectors is challenging.

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)0.001
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 score0.061
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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