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Record W2397582481 · doi:10.1109/jlt.2016.2574125

Silicon-Based On-Chip Electrically-Tunable Spectral Shaper for Continuously Tunable Linearly Chirped Microwave Waveform Generation

2016· article· en· W2397582481 on OpenAlexafffund
Weifeng Zhang, Jianping Yao

Bibliographic record

VenueJournal of Lightwave Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsFree spectral rangeMaterials scienceOpticsInterferometryOptoelectronicsMicrowaveWavelengthWaveformVoltagePhysics

Abstract

fetched live from OpenAlex

A silicon-based on-chip electrically-tunable spectral shaper for the generation of a tunable linearly chirped microwave waveform (LCMW) based on spectral shaping and wavelength-to-time (SS-WTT) mapping is designed, fabricated, and demonstrated. The on-chip spectral shaper has a Michelson interferometer structure with two linearly chirped waveguide Bragg gratings (LC-WBGs) incorporated in its two arms. Due to the wavelength-dependent length difference between the two arms of the interferometer, the spectral response of the spectral shaper exhibits a wavelength-dependent free spectral range, which is required for the generation of an LCMW based on SS-WTT mapping. To enable electrical tuning of the spectral response, a lateral PN junction is introduced to each of the waveguides where the LC-WBGs are inscribed. Thanks to the plasma dispersion effect, the spectral response of the spectral shaper can be tuned by changing the bias voltages applied to the PN junctions, which would lead to the tuning of the generated LCMW. A theoretical analysis on the LCMW generation is performed, which is verified by an experiment in which an electrically-tunable spectral shaper is fabricated using a CMOS-compatible process with 248-nm deep ultraviolet lithography. By independently controlling the bias voltages to the PN junctions, a continuous tuning of the generated LCMW is 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 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 score0.066
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.215
Teacher spread0.205 · 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

Citations46
Published2016
Admission routes2
Has abstractyes

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