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Record W2906820810 · doi:10.1109/jlt.2018.2889414

Electrically Programmable On-Chip Equivalent-Phase-Shifted Waveguide Bragg Grating on Silicon

2019· article· en· W2906820810 on OpenAlexafffund
Weifeng Zhang, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsFiber Bragg gratingGratingMaterials scienceOpticsPhase (matter)Blazed gratingFabricationWaveguideOptoelectronicsDiffraction gratingPhysics

Abstract

fetched live from OpenAlex

We report an electrically programmable equivalent-phase-shifted (EPS) waveguide Bragg grating implemented on silicon with programmable spectral response. Equivalent phase shift through nonuniform sampling in a Bragg grating is an effective solution to realize a phase-shifted Bragg grating, which significantly reduces the requirement for fabrication accuracy by three orders of magnitude as compared with the fabrication of a conventional phase-shifted Bragg grating. In this paper, an EPS Bragg grating with an equivalent phase shift introduced by increasing one sampling period in the grating center by a half sampling period is proposed, and the tuning of the phase shift is enabled by incorporating two independent PN junctions in each sampling period. Through controlling the bias voltages applied to the PN junctions, the spectral response of the EPS Bragg grating is tuned. The proposed EPS waveguide grating is fabricated and its performance is experimentally evaluated. A multichannel EPS Bragg grating with programmable spectral response is demonstrated. The key advantages of implementing EPS Bragg gratings include largely reduced fabrication constraint and strong multichannel tuning capability, which opens new avenues for on-chip Bragg gratings for programmable multichannel signal processing.

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 score0.142
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.252
Teacher spread0.241 · 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

Citations11
Published2019
Admission routes2
Has abstractyes

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