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Record W2027081070 · doi:10.1080/00207211003697863

A high-order curvature compensation technique for bandgap voltage reference using subthreshold MOSFETs

2010· article· en· W2027081070 on OpenAlexaff
Ahmad-Hossein Adl, Kamal El‐Sankary, E.I. El-Masry

Bibliographic record

VenueInternational Journal of Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBandgap voltage referencePMOS logicSubthreshold conductionNMOS logicVoltage referenceLine regulationMaterials scienceTransimpedance amplifierCMOSMOSFETElectrical engineeringVoltageElectronic engineeringOperational amplifierOptoelectronicsEngineeringTransistorAmplifierDropout voltage

Abstract

fetched live from OpenAlex

A bandgap voltage reference with high-order curvature compensation is presented in this study. It exploits subtraction and derivative equalisation of currents generated from two complementary NMOS and PMOS bandgap references (BGRs) using subthreshold MOSFETs. By equating the derivative with respect to temperature of the two currents, generated by the complementary bandgaps, and subtracting these currents, an accurate high-order curvature compensation is achieved. To overcome problems due to the limited input common-mode range of opamps used in BGRs, a transimpedance amplifier with new accurate current compensation that tracks the temperature variation is proposed. This bandgap is implemented using the 0.18 μm CMOS process with a supply voltage as low as 0.7 V. At 0.8 V power supply and an output reference voltage of 386 mV, the proposed circuit achieves a temperature coefficient of 19 ppm/°C from 0 to 130°C. The power consumption is 119 μW and the power supply reduction ratio is 24 dB at 1 kHz.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.545

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 integrity0.0000.001
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 head0.260
Teacher spread0.244 · 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
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

Citations5
Published2010
Admission routes1
Has abstractyes

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