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Record W4238014263 · doi:10.32920/ryerson.14645463.v1

A low-voltage CMOS current-mode differential front-end for optical communications

2021· preprint· en· W4238014263 on OpenAlexafffund
Bendong Sun

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsToronto Metropolitan University
FundersUniversity of Toronto
KeywordsCMOSPreamplifierInductorElectrical engineeringCurrent mirrorBandwidth (computing)Electronic engineeringDifferential amplifierVoltagePhysicsAmplifierEngineeringComputer scienceTransistorTelecommunications

Abstract

fetched live from OpenAlex

This thesis deals with the design of a low-voltage fully-differential CMOS current-mode preamplifier for optical communications. An in-depth comparative analysis of the building blocks of low-voltage CMOS current-mode circuits is carried out. Two new bandwidth enhancement techniques, namely inductor series-peaking and current feedback, are introduced and implemented in the design. The feedback also reduces the value of the series-peaking inductor. The minimum supply voltage of the amplifier is only one threshold voltage plus one pinch-off voltage. The preamplifier has a balanced differential topology such that the effect of bias dependent mismatches is minimized and the amplifier is insensitive to the switching noise caused by the digital circuitry. Negative differential current feedbacks are implemented to boost the bandwidth and increase the dynamic range.

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 score0.004
Threshold uncertainty score0.012

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.032
GPT teacher head0.294
Teacher spread0.261 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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