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Record W2784528297 · doi:10.1364/jocn.10.000065

Burst Mode Optical Receiver With 10  ns Lock Time Based on Concurrent DC Offset and Timing Recovery Technique

2018· article· en· W2784528297 on OpenAlexafffund
A K M Delwar Hossain, Fnu Aurangozeb, Masum Hossain

Bibliographic record

VenueJournal of Optical Communications and Networking · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsBurst mode (computing)JitterDC biasClock recoveryDuty cycleOffset (computer science)AmplifierTransimpedance amplifierDirect-conversion receiverComputer scienceElectronic engineeringElectrical engineeringPhysicsClock signalBandwidth (computing)VoltageDetectorEngineeringTelecommunicationsOperational amplifier

Abstract

fetched live from OpenAlex

This paper describes a low-power, low-latency, 7–10 Gb/s burst-mode DC-coupled receiver for photonic switch networks. The receiver includes a transimpedance amplifier (TIA) followed by a three-stage differential amplifier. For burst-mode operation, DC and timing recovery loops work concurrently in the proposed architecture to achieve 5.8 ns lock time. The DC recovery loop employs a successive approximation algorithm to recover signal-dependent offset that takes only six cycles of C8 (1/8th of data rate) clock. The timing recovery uses a quarter-rate injection scheme that is immune to duty cycle distortion. The recovered clock jitter is 10 ps p-p for 10 Gb/s operation. The receiver consumes only 33 mW while operating at 10 Gb/s, and less than 2 mW (leakage power and bias circuit) during idle time. The completely inductor-less receiver occupies a 465 μm×265 μm area in 0.13 μm technology.

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: none
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 communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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