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A Tunable CMOS Thyristor-Based Pulse Generator for Integrated Sensor Interface Applications

2020· article· en· W3089465231 on OpenAlexaff
Mahin Esmaeilzadeh, Mohamed R. Ali, Ahmad Hassan, Morteza Nabavi, Benoit Gosselin, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsUniversité LavalPolytechnique Montréal
Fundersnot available
KeywordsCMOSPulse generatorThyristorGenerator (circuit theory)Pulse (music)Electrical engineeringElectronic engineeringComputer scienceElectronic circuitSIGNAL (programming language)Pulse-width modulationSignal generatorIntegrated circuitPower (physics)EngineeringChipVoltagePhysics

Abstract

fetched live from OpenAlex

In this paper, a wide range, area efficient, and high-precision pulse generator is presented. The proposed architecture exploits a CMOS thyristor delay element, and benefits from its decent advantages. The proposed circuit generates an input-independent, stable, and accurate pulse width signal. The pulse-width can be tuned continuously in the range of 1.5 ns to 45 ms. This novel structure is a part of a control circuit intended for recognizing the eventual faults and errors in industrial sensor interfaces. The presented circuits have been implemented in 0.35 μm standard CMOS process. It consumes 0.11 to 7.42 mW power from a 3.3 V supply. The total occupied area is about 0.018 mm2and the maximum operation frequency of the proposed pulse generator is 331 MHz.

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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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