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Record W3212229486 · doi:10.1109/jsen.2021.3128573

A Small Footprint Digital Isolator Based on CMOS Integrated Hall-Effect Sensor

2021· article· en· W3212229486 on OpenAlexafffund
Seyed Sepehr Mirfakhraei, Yves Audet, Ahmad Hassan, Mohamad Sawan

Bibliographic record

VenueIEEE Sensors Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsIsolatorCMOSElectrical engineeringEngineeringElectronic engineeringAmplifierChipVoltageIntegrated circuitVoltage regulator

Abstract

fetched live from OpenAlex

Digital isolators have been widely used to protect low voltage electronics as well as human safety from high voltage surges. However, conventional isolation links occupy large chip areas. In this article, a novel small-size on- chip digital isolator for medium-bitrate application based on a CMOS integrated Hall-effect sensor is reported. With the proposed approach, the area of the transmitter coil is reduced to lower than 50% of conventional transformers. The architecture reduces the chip area in isolation amplifiers, power control units, DC-DC converters, and clock recovery circuits. It allows the integration of multichannel isolators using two custom integrated circuits with no post-processing. The tested prototype achieves a data transfer rate of 20 Mbps with above 12 kV/$\mu \text{s}$of common-mode transient immunity (CMTI). It has 900 V of continuous isolation working voltage, 27 ns propagation delay, and consumes 2.3 mA of static current.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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 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

Citations11
Published2021
Admission routes2
Has abstractyes

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Same venueIEEE Sensors JournalSame topicMagnetic Field Sensors TechniquesFrench-language works237,207