MétaCan
Menu
Back to cohort
Record W3196539000 · doi:10.1021/acsami.1c09775

Induced Complementary Resistive Switching in Forming-Free TiO<sub><i>x</i></sub>/TiO<sub>2</sub>/TiO<sub><i>x</i></sub> Memristors

2021· article· en· W3196539000 on OpenAlexafffund
Saurabh Srivastava, Joseph P. Thomas, Xiaoyi Guan, K. T. Leung

Bibliographic record

VenueACS Applied Materials & Interfaces · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceStackingResistive random-access memoryHeterojunctionMemristorOptoelectronicsLayer (electronics)VoltageActive matrixNanotechnologyElectronic engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The undesirable sneak current path is one of the key challenges in high-density memory integration for the emerging cross-bar memristor arrays. This work demonstrates a new heterojunction design of oxide multilayer stacking with different oxygen vacancy contents to manipulate the oxidation state. We show that the bipolar resistive switching (BRS) behavior of the Pt/TiO x /Pt cross-bar structure can be changed to complementary resistive switching (CRS) by introducing a thin TiO 2 layer in the middle of the TiO x layer to obtain a Pt/TiO x /TiO 2 /TiO x /Pt device architecture with a double-junction active matrix. In contrast to the BRS in a single-layer TiO x matrix, the device with a double-junction matrix remains in a high-resistance state in the voltage range below the SET voltage, which makes it an efficient structure to overcome the sneak path constraints of undesired half-selected cells that lead to incorrect output reading. This architecture is capable of eliminating these half-selected cells between the nearby cross-bar cells in a smaller programming voltage range. A simplified model for the switching mechanism can be used to account for the observed high-quality switching performance with excellent endurance and current retention properties.

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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

Citations37
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Materials & InterfacesSame topicAdvanced Memory and Neural ComputingFrench-language works237,207