MétaCan
Menu
Back to cohort
Record W2770144035 · doi:10.28991/ijse-01115

An Offset-free High linear Low Power High Speed Four-Quadrant MTL Multiplier

2017· article· en· W2770144035 on OpenAlexaff
HoseinAli Jafari, Zahra Abbasi, Seyed Javad Azhari

Bibliographic record

VenueEmerging Science Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCMOSAnalog multiplierLinearityMultiplier (economics)Electronic engineeringTotal harmonic distortionOffset (computer science)MATLABSpiceComputer sciencePower consumptionElectrical engineeringVoltageTopology (electrical circuits)Power (physics)EngineeringPhysicsAnalog signal

Abstract

fetched live from OpenAlex

In this paper a new CMOS current-mode four-quadrant analog multiplier circuit is proposed. The major advantages of this design are high linearity, high speed and low power consumption. Removing dc offset is the most important improvement in this topology. The circuit is designed with 1.8V supply voltage and is simulated using HSPICE simulator by level 49 parameters in 0.18µm standard CMOS TSMC technology. The aspect ratios of the MOSFETs are optimized using Evolutionary algorithm by MATLAB. The simulation results of this analog multiplier demonstrate a maximum linearity error of 2.6%, a THD of 1.77%, maximum power consumption of 157 µW, -3dB bandwidth of 241MHz and almost free from dc offset.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEmerging Science JournalSame topicAnalog and Mixed-Signal Circuit DesignFrench-language works237,207