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Record W2508150190 · doi:10.1109/pn.2016.7537935

Frequency down-conversion in GaAs enhanced by optical antennas

2016· article· en· W2508150190 on OpenAlexaff
Naser Abdulhavid Otman, Michael Čada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBirefringencePermittivityMaterials scienceQuasi-phase-matchingIsotropyEnergy conversion efficiencyPhase (matter)OpticsOptoelectronicsNonlinear opticsPhysicsComputational physicsDielectric

Abstract

fetched live from OpenAlex

Summary form only given. The nonlinear optical response of a material is relatively weak compared to the linear response, but nonlinear responses can be enhanced by increasing the power of the input frequencies to be mixed. Similarly, conversion efficiency can be enhanced by increasing the interaction volume of the structure. Phase-matching, which is a momentum conservation, is important for increasing the energy exchange between the waves. Fulfilling the phase-matching along the direction of the propagating waves will increase the power of the resulting wave of the mixing. GaAs is a cubic structure lattice which has isotropic optical properties. Because it is not possible to achieve birefringence phase-matching using GaAs, an artificial birefringence medium is made using other layers of different materials to obtain the phase-matching. Quasi-phase-matching is also possible by modulating the second-order susceptibility χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</sup> along the direction of the propagation to form grating. The two major techniques are domain-reversal quasi-phase-matching and domain-disordered quasi-phase-matching. This study focuses on frequency down-conversion in the mid-infrared range. Frequency down-conversion is an essential technique for generating mid-infrared optical frequencies when it is not possible to generate them by conventional sources. We study phase-matching using effective permittivity by inclusions of negative permittivity materials into GaAs, and investigate materials such as metals or doped GaAs. Gold is selected to be the inclusion material into GaAs as a host medium. Using the Maxwell-Garnett model of effective permittivity theories, we form the desired effective permittivity to obtain phase-matching. A one-dimensional optical antenna array is selected for the inclusions. By changing the inclusion volume fraction and depolarization factor of cylindrical optical antennas, we can specify effective permittivity in a certain direction. To simplify the analytical analysis of the problem, we assume the depression relations of the propagated waves in a bulk medium of GaAs. Omitting the power calculations, we achieve phase-matching for certain wavelengths, depending on the volume inclusions and deportation factors of the antennas rods. This result promises that phase-matching is possible in channel waveguides.

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 score0.168
Threshold uncertainty score0.304

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)0.0000.000

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 head0.205
Teacher spread0.199 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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