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Record W3156015335 · doi:10.1109/jlt.2021.3074096

Net 220 Gbps/λ IM/DD Transmssion in O-Band and C-Band With Silicon Photonic Traveling-Wave MZM

2021· article· en· W3156015335 on OpenAlexaff
Md Samiul Alam, Xueyang Li, Maxime Jacques, Zhenping Xing, Alireza Samani, Eslam El‐Fiky, Ping-Chiek Koh, David V. Plant

Bibliographic record

VenueJournal of Lightwave Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBit error rateBandwidth (computing)PhysicsModulation (music)L bandPhotonicsElectronic engineeringComputer scienceOpticsTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

We present the design and characterization of O-band and C-band silicon photonic (SiP) traveling wave Mach-Zehnder modulators (TW-MZM) allowing 220 Gbps/λ net rate operation. The designed modulators show over 45 GHz 3-dB E-O bandwidth with a single-segment design. In the O-band, with simple linear feed forward equalization, we transmit net 203 (200) Gbps signal over 2 km (10 km) of single-mode fiber (SMF) below the hard-decision forward error correction (HD-FEC) BER threshold of 3.8 × 10-3. With the aid of nonlinear Volterra equalizer and one 2.3Vppdriving signal, we transmit net 225 (216) Gbps PAM8 signals assuming 20% overhead soft-decision FEC with a normalized general mutual information (NGMI) threshold of 0.8798 over 2 km (10 km) of SMF. The C-band design enables net 220 Gbps in B2B and net 215 Gbps over 500 m of SMF above the specified NGMI threshold. These results are the highest reported net rate for SiP MZM in an intensity modulation direct-detection (IM/DD) system, fabricated entirely in a commercial foundry.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations64
Published2021
Admission routes1
Has abstractyes

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