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Record W3198645268 · doi:10.1109/tbcas.2021.3111784

A Versatile FG-MOSFET Inverter Design for X-Ray Radiation Dosimetry Applications

2021· article· en· W3198645268 on OpenAlex
Behzad Yadegari, Langis Roy, Farhan A. Ghaffar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsLakehead UniversityOntario Tech UniversityCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDosimetryMOSFETDosimeterInverterMaterials scienceTransistorElectrical engineeringRadiationElectronic engineeringOptoelectronicsVoltageEngineeringPhysicsOpticsNuclear medicineMedicine

Abstract

fetched live from OpenAlex

A novel inverter-based digital floating-gate MOSFET sensor design for commercial X-ray dosimetry is presented. The biomedical healthcare industry sterilizes blood products for storage purposes using Gamma and X-ray radiations. This requires an ultra-low-power dosimeter that ensures irradiation does not exceed the maximum allowable 50 Gy while providing the required minimum levels of 25 Gy. In this work, minimum-sized MOS transistor devices are employed in an inverter configuration, eliminating the continuous flow of current and reducing power consumption significantly. Maximum measured currents, which flow only during the transition period, are in the nA range, compared to continuous currents of conventional sensor designs in the μA range. Final measured results show the viability of the proposed design for radiation dosimetry applications.

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.

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: none
Teacher disagreement score0.996
Threshold uncertainty score0.663

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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