MétaCan
Menu
Back to cohort
Record W7028769127

High-speed optical receivers in nanometer complementary metal-oxide-semiconductor (CMOS)

2009· dissertation· en· W7028769127 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsMcGill University
FundersMcGill University
KeywordsTransimpedance amplifierCMOSGigabitBandwidth (computing)TransceiverVoltageAmplifierNoise figureOhmOptical power
DOInot available

Abstract

fetched live from OpenAlex

Optical interconnects have attracted great interest as data rates continue to increase.When compared with their electrical counterparts, optical interconnects have significant advantages in terms of crosstalk, bandwidth, distance, and latency.Many applications stand to benefit from low-cost, high-speed integrated optical transceivers with single-channel gigabit data rates.As in the case of RF wireless designs, using CMOS technology is of special interest due to the potential of lower cost and higher integration.The analog frontend is a key component in optical receivers due to its importance in bridging the optical and electrical signal domains.In this work, we present a 10 Gb/s optical receiver frontend designed and fabricated in ST 's 90 nm CMOS technology.The receiver contains a transimpedance (pre)amplifier (TIA), and limiting amplifier (LA), and an output buffer (OB).The TIA demonstrates a transimpedance gain of 61.9 dB and a bandwidth of 7.4 GHz, trading off noise and ISI considerations.The single-ended design utilizes 1.5 mW of power from a 1.0 V supply.The LA demonstrates a voltage gain of 21 dB and a bandwidth extended to 10 GHz using inductive peaking.The differential design utilizes 3.9 mW of power from a 1.0 V supply.Finally, the output buffer is capable of driving large output voltage swings to 50 on-chip terminations.In order to test the receiver, a PCB and testing strategy is co-designed with the chip.Details concerning the various design decisions, tradeoffs, are discussed in this thesis.Experimental results of a fabricated device are presented under ideal and practical system levels, with data rates up to 8.5 Gb/s.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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 designTheoretical or conceptual
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
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicMilitary History and StrategyFrench-language works237,207