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Record W2077345246 · doi:10.1109/tcsii.2013.2273839

A 4-Gbps POF Receiver Using Linear Equalizer With Multi-Shunt-Shunt Feedbacks in 65-nm CMOS

2013· article· en· W2077345246 on OpenAlexafffund
Yunzhi Dong, Kenneth W. Martin

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2013
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsTransimpedance amplifierCMOSAmplifierChipGigabitShunt (medical)Electrical engineeringVariable-gain amplifierElectronic engineeringComputer sciencePhysicsDifferential amplifierEngineeringOperational amplifier

Abstract

fetched live from OpenAlex

This brief describes the design of a monolithic plastic optical fiber (POF) receiver with a pair of 250-by-250 μm N-well/P-sub photodetectors (PDs). A two-stage continuous-time linear equalizer that utilizes multiple active shunt-shunt feedback networks has been proposed to compensate for the slow-rolling-off high-frequency losses of the PDs. A test chip has been implemented in a standard 65-nm CMOS process, and it consists of a transimpedance amplifier, a variable-gain amplifier, a linear equalizer, a limiting amplifier, and an output buffer. The receiver consumes an active chip area of 0.24 mm2and a dc power of 46 mW (excluding auxiliary test circuits and the output buffer) from a 1-V power supply. The prototype POF receiver demonstrates a non-return-to-zero data rate of 4 Gbit/s with a bit error rate less than 10-12, at a peak-to-peak optical input power of - 3.2 dBm p-p (average input power is kept at -3 dBm).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0020.001

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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations27
Published2013
Admission routes2
Has abstractyes

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