MétaCan
Menu
Back to cohort
Record W1546705134 · doi:10.1109/asic.2002.1158055

Power reduction via an MTCMOS implementation of MOS current mode logic

2003· article· en· W1546705134 on OpenAlexaff
Mohab Anis, M.I. Elmasry

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCMOSCurrent-mode logicElectronic engineeringReduction (mathematics)Logic gateComputer sciencePower (physics)Low-power electronicsVoltageThreshold voltageElectrical engineeringVoltage reductionEngineeringTransistor

Abstract

fetched live from OpenAlex

In this paper, MOS current mode logic (MCML) is implemented in a multi-threshold CMOS technology. The design scheme allows the reduction of the minimum operational supply voltage, as well as the elimination of level shifters used. A high-speed 1:8 2.5 Gbit/s demultiplexer is used as a test vehicle. A 37% power saving is achieved. Furthermore, the impact of using the MTCMOS implementation over circuit parameters such as output impedance, current-matching-ratio, common-mode-rejection-ratio, gain, threshold voltage fluctuations and frequency response is investigated. This work is important for industry since the MTCMOS technique opens a new alley for reducing power in MCML high-speed implementations while attaining the required data rates.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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
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

Citations31
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicLow-power high-performance VLSI designFrench-language works237,207