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Record W2032285114 · doi:10.1109/tvlsi.2014.2322511

On the Nonvolatile Performance of Flip-Flop/SRAM Cells With a Single MTJ

2014· article· en· W2032285114 on OpenAlexaff
Ke Chen, Jie Han, Fabrizio Lombardi

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlip-flopStatic random-access memoryNon-volatile memoryTransistorElectrical engineeringMOSFETElectronic engineeringDissipationData retentionVery-large-scale integrationTunnel magnetoresistancePower (physics)Resistive touchscreenNoise marginCMOSComputer scienceMaterials scienceVoltageEngineeringNanotechnologyPhysics

Abstract

fetched live from OpenAlex

In this brief, three nonvolatile flip-flop (FF)/SRAM cells that utilize a single magnetic tunneling junction (MTJ) as nonvolatile resistive element are proposed. These cells have the same core (i.e., 6T) but they employ different numbers of MOSFETs to implement the so-called instantly ON, normally OFF mode of operation. The additional transistors are utilized for the restore operation to ensure that the data stored in the nonvolatile circuitry can be written back into the FF core once the power is made available. These three cells (7T, 9T, and 11T) are extensively analyzed in terms of their operations in 32 nm technology, such as operational delays (for the write, read, and restore operations), the static noise margin (SNM), critical charge and process variations (in both the MOSFETs and the resistive element). Simulation results show that an increase in the number of MOSFETs in the cells causes improvements in critical charge and tolerance to process variations at the expense of an increase in power dissipation. The SNM and the delay of the restore operation, however, do not necessarily increase with the number of MOSFETs in the cell, but rather on the control of access to the storage nodes from the single MTJ.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.648

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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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 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
Published2014
Admission routes1
Has abstractyes

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