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Nanoscale memory devices

2010· article· en· W2127570561 on OpenAlexaff
Andy Chung, M. Jamal Deen, Jeong‐Soo Lee, M. Meyyappan

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsResistive random-access memoryMaterials scienceMemristorFlash memoryNanotechnologyPhase-change memoryRandom access memoryMagnetoresistive random-access memoryNon-volatile memoryNanoscopic scaleRandom accessComputer memoryNanomaterialsSemiconductor memoryOptoelectronicsComputer scienceElectrical engineeringEmbedded systemComputer hardwareEngineering

Abstract

fetched live from OpenAlex

This article reviews the current status and future prospects for the use of nanomaterials and devices in memory technology. First, the status and continuing scaling trends of the flash memory are discussed. Then, a detailed discussion on technologies trying to replace flash in the near-term is provided. This includes phase change random access memory, Fe random access memory and magnetic random access memory. The long-term nanotechnology prospects for memory devices include carbon-nanotube-based memory, molecular electronics and memristors based on resistive materials such as TiO(2).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.010

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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
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

Citations113
Published2010
Admission routes1
Has abstractyes

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