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Record W2902219687 · doi:10.1109/jphot.2018.2884170

Tunable Silicon Photonic RF Phase Shifter With Low RF Power Variation Based on Constructive Interference of an Add-Drop Ring Resonator

2018· article· en· W2902219687 on OpenAlexaff
Yanping Yu, Qihang Shang, Shaohua An, Yong Zhang, Yikai Su, Jianping Yao

Bibliographic record

VenueIEEE photonics journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Ottawa
FundersShanghai Jiao Tong UniversityScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsResonatorPhase shift moduleRadio frequencyDrop (telecommunication)Materials scienceOptoelectronicsOpticsRF power amplifierOptical ring resonatorsPhase (matter)Power dividers and directional couplersPhysicsElectrical engineeringInsertion lossEngineering

Abstract

fetched live from OpenAlex

We propose and experimentally demonstrate a tunable silicon photonic radio frequency (RF) phase shifter with low power variation based on constructive interference of an add-drop ring resonator. The thermal nonlinear effect of the add-drop ring resonator is used to realize a nearly 2π-phase shift, and the RF power variation is minimized by allowing two lights at the outputs of the add-drop ring resonator to combine at a directional coupler to achieve constructive interference. By thermally tuning the add-drop ring resonator, a nearly 2π-phase shift is realized and the operation is verified by an experiment. Experimental results show that a 0-5.2-rad phase-shift range with an RF power variation within 0.2 dB for a frequency range of a 40-GHz signal is achieved by thermally tuning the ring resonator. A phase shift up to 4.1 rad over a frequency range of 20-40 GHz is also realized.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.263
Teacher spread0.251 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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