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Record W2897736766 · doi:10.3390/app8101858

Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide

2018· article· en· W2897736766 on OpenAlexaff
Qiuyue Zhang, Xun Li

Bibliographic record

VenueApplied Sciences · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotonic crystalSlow lightScatteringOpticsMaterials scienceSquare latticeLight scatteringWaveguideHeterojunctionPhotonicsOptoelectronicsBandwidth (computing)PhysicsCondensed matter physicsTelecommunications

Abstract

fetched live from OpenAlex

In conventional photonic crystal systems, extrinsic scattering resulting from random manufacturing defects or environmental changes is a major source of loss that causes performance degradation, and the backscattering loss is amplified as the group velocity slows down. In order to overcome the limitations in slow light systems, we propose a backscattering-immune slow light waveguide design. The waveguide is based on an interface between a square lattice of magneto-optical photonic crystal with precisely tailored rod radii of the first two rows and a titled 45 degrees square lattice of Alumina photonic crystal with an aligned band gap. High group indices of 77, 68, 64, and 60 with the normalized frequency bandwidths of 0.444%, 0.481%, 0.485%, and 0.491% are obtained, respectively. The corresponding normalized delay-bandwidth products remain around 0.32 for all cases, which are higher than previously reported works based on rod radius adjustment. The robustness for the edge modes against different types of interfacial defects is observed for the lack of backward propagation modes at the same frequencies as the unidirectional edge modes. Furthermore, the transmission direction can be controlled by the sign of the externally applied magnetic field normal to the plane.

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 score0.206
Threshold uncertainty score0.987

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)0.0010.000

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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