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Record W2009211361 · doi:10.1109/igcc.2013.6604483

An efficiency enhancement technique for CMOS rectifiers with low start-up voltage for UHF RFID tags

2013· article· en· W2009211361 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)CMOSPeak inverse voltageNMOS logicPMOS logicElectrical engineeringTransistorVoltagePrecision rectifierLow voltageElectronic engineeringComputer scienceEngineeringVoltage dividerPower factorDropout voltage

Abstract

fetched live from OpenAlex

A high-efficiency CMOS rectifier with low start-up voltage for ultra-high-frequency (UHF) radio-frequency identification (RFID) applications is presented. To enhance the power conversion efficiency (PCE) of the conventional rectifier when the input voltage (power) is low, appropriate gate-drive voltages for each stage of the rectifier are generated using a chain of auxiliary floating rectifier cells. Floating rectifier cells are optimized to generate shifted versions of the intermediate voltage of each stage to boost the drive voltage of NMOS and PMOS switching transistors and accordingly improve the PCE. The proposed rectifier architecture is designed in a standard 0.13μm CMOS technology. For a 950 MHz RF input and 50 kΩ output load, simulation results show that the rectifier achieves a PCE of 54% for a small input signal with an amplitude of 200 mV (-19 dBm) which is well below the nominal standard threshold voltage of MOS transistors in the technology used.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.875

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations21
Published2013
Admission routes1
Has abstractyes

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