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Record W7117236838 · doi:10.1109/lpt.2025.3648662

Coherent Transceivers Using SOA–Comb Architectures for Short-Reach Links Beyond 1 Tb/s

2025· article· W7117236838 on OpenAlexfundno aff
Arman Safarnejadian, Wei Shi, Leslie A. Rusch, Ming Zeng

Bibliographic record

VenueIEEE Photonics Technology Letters · 2025
Typearticle
Language
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransceiverPhotonicsSilicon photonicsModulation (music)Optical amplifierAmplifierPower (physics)Bit error rateOptical performance monitoring

Abstract

fetched live from OpenAlex

We investigate power-efficient multi-wavelength coherent transceivers for short-reach optical interconnects targeting 1.2 Tb/s throughput. A single optical frequency comb can be used for both data modulation and distributed local oscillators. This comb-assisted architecture can be combined with integrated semiconductor optical amplifiers (SOAs) and silicon photonic traveling-wave Mach-Zehnder modulators (TW-MZMs). We analyze two configurations for this combination, placing the SOAs either before or after the modulators. Our results show that positioning SOAs after the modulators enhances overall power efficiency, while longer (4 mm) modulators provide superior power handling due to lower total loss while providing a sufficient bandwidth. Furthermore, by adjusting the power-splitting ratio between the data carrier and the distributed local oscillator, the minimum achievable bit error rate (BER) can be tuned in the post-modulation SOA configuration. These findings offer design insights for optimizing coherent multi-wavelength transceivers based on silicon photonics for next-generation intra-datacenter links.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
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 score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0040.005
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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