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Record W2031022629 · doi:10.1109/rfid-ta.2013.6694527

A CMOS rectifier with an extended high-efficiency region of operation

2013· article· en· W2031022629 on OpenAlexaff
Pouya Kamalinejad, Kamyar Keikhosravy, Shahriar Mirabbasi, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRectifier (neural networks)CMOSElectrical engineeringVoltageTransistorBiasingdBmPower (physics)Electronic engineeringPower semiconductor deviceEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

A CMOS rectifier with a wide input signal range for radio-frequency identification (RFID) applications is presented. Using quasi-floating gate technique, a gate-biasing scheme is proposed to provide a relatively flat power conversion efficiency (PCE) curve for a wide input voltage (power) range. The proposed technique also enables an efficient operation for input voltage levels well below the standard threshold voltage of the MOS switching transistors. Appropriate bias voltages for different stages of the rectifier are generated through a chain of low-power bandgap reference generators which impose minimal power and area overhead. The proposed rectifier architecture is designed and laid out in a standard 0.13-μm CMOS technology. For a 2.4 GHz RF input frequency and 30 kΩ output load, post-layout simulation results of the circuit show that a maximum PCE of 66.7% is achieved for an input signal with an amplitude (power) of 0.45 V (-8 dBm). While a high PCE of 60% is achieved for input voltage (power) levels as low as 0.25 V (-15 dBm), PCE maintains above 60% for a wide input voltage (power) range from 0.25 V to 0.7 V (-15 dBm to -3 dBm).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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.

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

Citations13
Published2013
Admission routes1
Has abstractyes

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