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Record W2421573266 · doi:10.1109/jlt.2015.2480101

Crosstalk in SOI Microring Resonator-Based Filters

2015· article· en· W2421573266 on OpenAlexafffund
Hasitha Jayatilleka, K. Murray, Michael Caverley, Nicolas A. F. Jaeger, Lukas Chrostowski, Sudip Shekhar

Bibliographic record

VenueJournal of Lightwave Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsResonatorSilicon on insulatorWavelength-division multiplexingCrosstalkMultiplexingElectronic engineeringOpticsMaterials scienceOptoelectronicsWavelengthPhysicsEngineeringSilicon

Abstract

fetched live from OpenAlex

We experimentally investigate the interchannel and intrachannel crosstalk of first- and second-order microring resonator (MRR) filters fabricated on a silicon-on-insulator platform. We find that there is an MRR radius that maximizes the wavelength division multiplexing channel count given a waveguide geometry, a maximum tolerable insertion loss, and a minimum permissible adjacent channel isolation. The measured power penalties due to interchannel crosstalk of two-channel demultiplexers based on first-order and series-coupled MRR filters are presented as functions of channel spacing and adjacent channel isolation. Next, we compare the intrachannel crosstalk of first-order, cascaded, and series-coupled MRR add-drop filters. Our results show that first-order MRR devices are unsuitable for simultaneous add-drop operation at high data rates and small channel spacings. Intrachannel crosstalk of cascaded and series-coupled designs are measured as functions of the data rate and the level of detuning between the MRRs. Low intrachannel crosstalk power penalties are demonstrated for cascaded and series-coupled MRR filters for data rates up to 20 Gb/s. Based on the measured results, we present requirements for the input-to-through response of add-drop filters that will ensure low intrachannel crosstalk.

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 score0.179
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations53
Published2015
Admission routes2
Has abstractyes

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