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Record W2081996288 · doi:10.1109/jlt.2014.2377657

Independently Tunable Multichannel Fractional-Order Temporal Differentiator Based on a Silicon-Photonic Symmetric Mach–Zehnder Interferometer Incorporating Cascaded Microring Resonators

2014· article· en· W2081996288 on OpenAlexafffund
Weifeng Zhang, Weilin Liu, Wangzhe Li, Hiva Shahoei, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDifferentiatorResonatorSilicon photonicsInterferometryPhotonicsMaterials scienceMach–Zehnder interferometerOpticsLithographyOptoelectronicsPhysicsBandwidth (computing)TelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A multichannel fractional-order temporal differentiator with independently tunable differentiation order based on an integrated silicon-photonic symmetric Mach-Zehnder interferometer consisting of cascaded microring resonators (MRRs) is designed, fabricated, and experimentally demonstrated. By controlling the radii of the MRRs, a multichannel spectral response with uniform channel spacing is obtained, which is used to function as a multichannel temporal differentiator with multiple subdifferentiators. The differentiation order of each subdifferentiator is independently tunable by optically pumping the corresponding MRR, which leads to the phase change in the spectral response due to the two-photon absorption induced nonlinear effect. A five-channel temporal differentiator with a channel spacing of 0.49 nm is fabricated on a silicon-on-insulator chip using a CMOS-compatible process with 193-nm-deep ultraviolet lithography. Independent tuning of the differentiation orders of the subdifferentiators is experimentally demonstrated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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.

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
Published2014
Admission routes2
Has abstractyes

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