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Record W1967302898 · doi:10.1109/tcsi.2014.2332247

A Survey on Circuit Modeling of Spin-Transfer-Torque Magnetic Tunnel Junctions

2014· article· en· W1967302898 on OpenAlexafffund
Aynaz Vatankhahghadim, Safeen Huda, Ali Sheikholeslami

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpin-transfer torqueTunnel magnetoresistanceMagnetoresistive random-access memoryTorqueElectronic circuitSpin (aerodynamics)Electronic engineeringMagnetoresistanceComputer scienceElectrical engineeringEngineeringMaterials sciencePhysicsMechanical engineeringRandom access memoryComputer hardwareNanotechnologyMagnetizationMagnetic field

Abstract

fetched live from OpenAlex

Accurate modeling of magnetic tunnel junction (MTJ) is critical for design of memories such as spin-transfer-torque magnetoresistive random access memory (STT-MRAM) and spin logic circuits such as spin flip flops. This paper reviews several static and dynamic models for the MTJ and compares them for their capabilities and limitations. Furthermore, a Verilog-A model is developed to predict dynamic characteristics of the MTJ. These models are used in simulating a prototype circuit to illustrate their strengths and weaknesses.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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 designSimulation or modeling
Domainnot available
GenreReview

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

Citations41
Published2014
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Circuits and Systems I Regular PapersSame topicMagnetic properties of thin filmsFrench-language works237,207