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Record W2099636456 · doi:10.1109/cicc.2011.6055362

A monolithic 3.125 Gbps fiber optic receiver front-end for POF applications in 65 nm CMOS

2011· article· en· W2099636456 on OpenAlexafffund
Yunzhi Dong, Ken Martin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsTransimpedance amplifierCMOSFront and back endsGigabitDetectorRF front endElectrical engineeringAmplifierElectronic engineeringComputer scienceEngineeringOperational amplifierRadio frequency

Abstract

fetched live from OpenAlex

This paper describes the design of an analog receiver front-end targeting multi-gigabit data communications over large-core plastic optical fibers. A receiver front-end with an integrated photo detector has been implemented in a standard TSMC 65 nm low-power bulk-silicon CMOS process. A novel hybrid current buffer based transimpedance amplifier has been proposed to drive the 14 pF photo capacitance presented by the large-area photo detector to multi-gigahertz range. A digitally controlled slow-slope equalizer has also been integrated in the receiver front-end to compensate the high-frequency losses due to the integrated photo detector. The receiver front-end consumes 50 mW dc power from a 1.2 V supply (excluding output buffer) and achieves an NRZ data rate up to 3.125 Gbps.

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 score0.003
Threshold uncertainty score0.010

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.027
GPT teacher head0.222
Teacher spread0.195 · 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

Citations15
Published2011
Admission routes2
Has abstractyes

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