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Record W4322576789 · doi:10.1109/jlt.2023.3250112

Segmented Silicon Modulator With a Bandwidth Beyond 67 GHz for High-Speed Signaling

2023· article· en· W4322576789 on OpenAlexfundno aff
Abdolkhalegh Mohammadi, Zibo Zheng, Xiaoguang Zhang, Leslie A. Rusch, Wei Shi

Bibliographic record

VenueJournal of Lightwave Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsBandwidth (computing)Quadrature amplitude modulationOptical filterElectronic engineeringOptical modulatorOptical modulation amplitudePhysicsModulation (music)Computer scienceOpticsElectro-optic modulatorPhase-shift keyingPhase modulationBit error rateEngineeringTelecommunicationsOptical amplifierDecoding methods

Abstract

fetched live from OpenAlex

We demonstrate an all-silicon segmented modulator with traveling-wave electrodes, achieving an electro-optic bandwidth beyond 67 GHz and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V_\pi$</tex-math></inline-formula> of 5 V. We show that dividing a long optical modulator into shorter segments can help achieve a higher bandwidth without compromising modulation efficiency or optical loss. We analyze the bandwidth limitation due to the delay mismatch between the driving and optical signals. We show that the impact of misalignment of driving delays between segments on the overall bandwidth of the modulator can be described by a finite impulse response filter. Using this modulator, we achieve optical transmission of 120 Gbaud 8-level amplitude shift keying with coherent detection. Taking into account the forward error correction overhead, this yields 336.4 Gb/s net on a single polarization. Our design has the potential for achieving 1 Tb/s using dual-polarization IQ modulation.

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 score0.056
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations51
Published2023
Admission routes1
Has abstractyes

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