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Record W2109073996 · doi:10.1109/ismvl.2015.38

A Multi-level Cell for STT-MRAM with Biaxial Magnetic Tunnel Junction

2015· article· en· W2109073996 on OpenAlexafffund
Aynaz Vatankhahghadim, Ali Sheikholeslami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetoresistive random-access memoryTunnel magnetoresistanceMaterials scienceOptoelectronicsComputer scienceRandom access memoryComposite materialComputer hardware

Abstract

fetched live from OpenAlex

A multi-level cell for STT-MRAM is proposed using biaxial magnetic tunnel junction (MTJ). The proposed cell consists of one transistor and one MTJ (1T1MTJ) with biaxial magnetic layer to store two bits per cell. Using the four stable states of the biaxial layer, the proposed cell allows 2 bits to be stored per cell without voltage headroom limitations. Current pulses with different amplitudes are applied during write operation to switch the magnetization vector to the corresponding region. This avoids multi-step write operations required for previously proposed multi-level cells using uniaxial MTJs. On average, the simulated write speed of the proposed cell is 33% faster than that of previous work, and the proposed cell consumes 8% less power. Also, current sensing vs. voltage sensing is compared for the biaxial MTJ, Current sensing provides uniform distribution of the sense margin.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score0.943

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)0.0010.000

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 head0.067
GPT teacher head0.240
Teacher spread0.172 · 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 designNot applicable
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

Citations8
Published2015
Admission routes2
Has abstractyes

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Same topicMagnetic properties of thin filmsFrench-language works237,207