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Record W2947663591 · doi:10.1109/jlt.2019.2910491

Silicon Photonic IQ Modulators for 400 Gb/s and Beyond

2019· article· en· W2947663591 on OpenAlexafffund
Hassan Sepehrian, Jiachuan Lin, Leslie A. Rusch, Wei Shi

Bibliographic record

VenueJournal of Lightwave Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuadrature amplitude modulationPhotonicsSilicon photonicsQAMBaudBandwidth (computing)Modulation (music)Phase modulationOpticsElectronic engineeringPhysicsTransmission (telecommunications)Computer scienceBit error rateTelecommunicationsPhase noiseEngineeringDecoding methods

Abstract

fetched live from OpenAlex

Silicon photonics has enormous potential for ultrahigh-capacity coherent optical transceivers. We demonstrate an in-phase and quadrature (IQ) modulator using silicon photonic traveling-wave modulators optimized for higher order quadrature amplitude modulation (QAM). Its optical and RF characteristics are studied thoroughly in simulation and experiment. We propose a system-orientated approach to optimization of the silicon photonic IQ modulator, which minimizes modulator-induced power penalty in a QAM transmission link. We examine the tradeoff between modulation efficiency and bandwidth for the optimal combination of modulator length and bias voltage to maximize the clear distance between adjacent constellation points. This optimum depends on baud rate and modulation format, as well as achievable driving voltage swing. Measured results confirm our prediction using the proposed methodology. Without precompensating bandwidth limitation of the modulator, net data rates up to 232 Gb/s (70 Gbaud 16-QAM) on single polarization are captured, indicating great potential for 400+ Gb/s dual-polarization transmission.

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 score0.147
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.206
Teacher spread0.201 · 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
Published2019
Admission routes2
Has abstractyes

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