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Record W2082770563 · doi:10.1149/2.100406jes

Polymer-Based Memory Structures on Copper Substrates

2014· article· en· W2082770563 on OpenAlexaff
Iman Yahyaie, Tanya Khaper, Patrick K. Giesbrecht, Michael S. Freund

Bibliographic record

VenueJournal of The Electrochemical Society · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceTransistorTungstenOxideNanotechnologyCMOSPolymerOptoelectronicsCapacitorCopperVoltageElectrical engineeringMetallurgyComposite materialEngineering

Abstract

fetched live from OpenAlex

Considering the limitations of dynamic memory structures based on the "1 Transistor –1 Capacitor" design paradigm related to their complexity and scaling, non-transistor based designs are being actively pursued. Bulk resistive memory designs that do not require additional transistor components per bit, are desirable candidates. This article presents a layered approach for formation of tungsten oxide and conducting polymer junctions to be used as a redox memory structure. This system includes an electrochemically deposited tungsten oxide film covered by electrophoretically deposited conducting polymer material capable of producing transient current-voltage characteristics that can be controlled by electric fields and exhibiting memory effects. The approach presented in this paper is particularly attractive since it provides a means to form these junctions on CMOS-friendly contact metals such as copper. Junctions between tungsten oxide and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) have been formed and characterized, demonstrating these junctions on copper substrates, significantly advancing this approach for making CMOS-compatible crossbar memory structures based on conducting polymer systems.

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 score0.002
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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