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Record W4285264555 · doi:10.1109/jlt.2022.3185014

Silicon Photonic Integrated Fano Resonator With Increased Slope Rate for Microwave Signal Processing

2022· article· en· W4285264555 on OpenAlexafffund
Zheng Dai, Peng Li, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsResonatorFano resonanceOpticsMaterials scienceExtinction ratioOptoelectronicsFree spectral rangeMicrowaveSIGNAL (programming language)PhysicsPlasmonWavelength

Abstract

fetched live from OpenAlex

A tunable silicon photonic (SiP) integrated Fano resonator with an increased slope rate for microwave signal processing is proposed, fabricated, and experimentally demonstrated. A grating-based Fabry-Perot (FP) cavity-coupled micro-ring resonator (MRR) joint with a Mach-Zehnder Interferometer (MZI) is used to form the proposed Fano resonator. Since the resonant modes of the FP cavity and the MZI interfere with that of the MRR, a Fano resonator with an ultra-sharp asymmetric line shape is realized. A microheater is placed on top of the MRR to achieve thermal tuning of the Fano resonance frequency. The proposed Fano resonator is fabricated and experimentally evaluated. A high slope rate (SR) of 379.08 dB/nm and an extinction ratio (ER) of 22.97 dB are obtained. Thanks to its ultra-sharp asymmetric line shape, the applications of the proposed Fano resonator for microwave signal processing are discussed and experimentally demonstrated, including instantaneous frequency measurement with an improved resolution of ±0.2 GHz in a 7 GHz frequency measurement range, and optical temporal differentiation with an increased differentiation gain.

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 score0.154
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.209
Teacher spread0.202 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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