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Record W2017797626 · doi:10.1109/jqe.2002.807185

Design of deeply etched antireflective waveguide terminators

2003· article· en· W2017797626 on OpenAlexaff
Gui-Rong Zhou, Xun Li, Ning-Ning Feng

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsMcMaster University
FundersNanjing University
KeywordsAnti-reflective coatingOpticsTransfer-matrix method (optics)Materials scienceWaveguideEtching (microfabrication)ComputationFinite difference methodFinite-difference time-domain methodBoundary value problemGratingOptoelectronicsPerfectly matched layerInterference (communication)Computer scienceLayer (electronics)PhysicsTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

An alternative solution to achieve an antireflective waveguide terminator is proposed by adopting a deeply etched waveguide structure to replace the conventional facet interference coatings. The performance is evaluated by different numerical approaches and optimum designs can be achieved based on the combination of the finite-difference time-domain method and the transfer matrix method. Perfectly matched layer absorbing boundary conditions are employed and pre-optimized in order to eliminate any nonphysical reflections due to the computation window introduced artificially. Results show that a power reflectivity of less than 5.0×10/sup -3/ over almost the entire C-band with a minimum value as low as 1×10/sup -5/ can be achieved. The effects on etching with a tilted angle and etching with finite depth are also studied.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2003
Admission routes1
Has abstractyes

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