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Record W4319998019 · doi:10.1109/tvlsi.2023.3238286

A Power-Proportional, Dual-Bandwidth, and Constant-Delay Receiver Front-End for Energy-Efficient Dual-Rate Optical Links

2023· article· en· W4319998019 on OpenAlexafffund
Abdullah Ibn Abbas, Xiangdong Jia, Glenn Cowan

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsTransimpedance amplifierBandwidth (computing)CMOSElectronic engineeringComputer scienceAmplifierLow-power electronicsPhysicsOffset (computer science)Electrical engineeringOperational amplifierOptoelectronicsPower (physics)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

This article presents a dual-bandwidth front-end (FE) for a rapidly reconfigurable dual data rate and power-proportional optical receiver. The proof-of-concept receiver FE is capable of operation at 8- and 4-Gb/s data rates. Implemented in 65-nm CMOS technology, the proposed FE consists of a shunt-feedback transimpedance amplifier (TIA), a configurable one-stage-to-three-stage postamplifier (PA), and an offset compensation (OC) loop. By reconfiguring the number of stages in the PA, the FE maintains a near-constant delay when its bandwidth is changed. This allows synchronization to be maintained, with limited bit errors when the target data rate is switched. The prototype receiver was measured with an optical input at 8 and 4 Gb/s. The overall FE dissipates 6.12 mW at 8 Gb/s (0.76 pJ/bit) and 2.86 mW at 4 Gb/s (0.72 pJ/bit). The measured receiver optical sensitivities for 8- and 4-Gb/s inputs at high-bandwidth (HBW) and low-bandwidth (LBW) modes are −7.7 and −9.8 dBm, respectively. The measurement results confirm the matched delay through the FE with delay variations within 8 ps.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations1
Published2023
Admission routes2
Has abstractyes

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