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Record W1978325717 · doi:10.1109/jphot.2015.2426875

A Low-Voltage 35-GHz Silicon Photonic Modulator-Enabled 112-Gb/s Transmission System

2015· article· en· W1978325717 on OpenAlexafffund
Alireza Samani, Mathieu Chagnon, David Patel, Venkat Veerasubramanian, Samir Ghosh, Mohamed Morsy-Osman, Qiuhang Zhong, David V. Plant

Bibliographic record

VenueIEEE photonics journal · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsTransmitterModulation (music)PhotonicsPhysicsTransmission (telecommunications)Pulse-amplitude modulationPhase modulationBandwidth (computing)Electrical impedanceSilicon photonicsOptoelectronicsElectro-optic modulatorQuadrature amplitude modulationVoltageOpticsOptical modulatorTelecommunicationsComputer sciencePulse (music)Phase noiseBit error rate

Abstract

fetched live from OpenAlex

We present a silicon photonic traveling-wave Mach-Zehnder modulator operating near 1550 nm with a 3-dB bandwidth of 35 GHz. A detailed analysis of travelingwave electrode impedance, microwave loss, and phase velocity is presented. Small-and large-signal characterization of the device validates the design methodology. We further investigate the performance of the device in a short-reach transmission system. We report a successful 112-Gb/s transmission of four-level pulse amplitude modulation over 5 km of SMF using 2.2 Vp_pdrive voltage. Digital signal processing is applied at the transmitter and receiver. 56-GBaud PAM-4 and 64-Gb/s PAM-2 transmission is demonstrated below a pre-FEC hard decision threshold of 4.4 x 10-3.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.015
GPT teacher head0.221
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations97
Published2015
Admission routes2
Has abstractyes

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