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Record W2973268939 · doi:10.1039/c9nr06403f

Programmable, electroforming-free TiO<sub>x</sub>/TaO<sub>x</sub> heterojunction-based non-volatile memory devices

2019· article· en· W2973268939 on OpenAlexafffund
Saurabh Srivastava, Joseph P. Thomas, K. T. Leung

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroformingMaterials scienceHeterojunctionOptoelectronicsNanotechnology

Abstract

fetched live from OpenAlex

Electroforming-free resistive switching in memristors is essential to reliably achieving the performance of high switching speed, high endurance, good signal retention, and low power consumption expected for next-generation computing devices. Although there have been various approaches to resolve the issues observed with the electroforming process in oxide-based memory devices, most of them end up having high SET and RESET voltages and short lifetimes. We present a heterojunction interface of oxygen-vacancy-defect-rich ultrananocrystalline TiOx and TaOx films used as the switching matrix, which enables high-quality electroforming-free switching with a much lower programming voltage (+0.5-0.8 V), a high endurance of over 104 cycles and good retention performance with an estimated device lifetime of over 10 years. The electroforming-free switching behavior is governed by migration of oxygen vacancies driven by electric field localization that is imposed by the ultrananocrystalline nature of the TaOx film, serving as the switching matrix, with the TiOx film serving as an additional oxygen vacancy source to reduce the overall resistivity of TaOx and provide low-bias rectification. The ability to perform electroforming-free resistive switching along with excellent switching repeatability and retention capabilities for various digital and analog programmable voltages enables high scalability and large density integration of the cross-bar ReRAM framework.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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
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

Citations27
Published2019
Admission routes2
Has abstractyes

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