MétaCan
Menu
Back to cohort
Record W2792110548 · doi:10.1002/adfm.201706230

UV‐Induced Multilevel Current Amplification Memory Effect in Zinc Oxide Rods Resistive Switching Devices

2018· article· en· W2792110548 on OpenAlexaff
Paola Russo, Ming Xiao, Robert Liang, Y. Zhou

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceOptoelectronicsUltravioletResistive random-access memoryElectrodeResistive touchscreenSemiconductorRodOxideZinc

Abstract

fetched live from OpenAlex

Abstract Zinc oxide (ZnO) devices represent an alternative in the semiconductor technology for their application in resistive switching memory devices and ultraviolet (UV) photodetectors due to their chemical and electrical properties. The multilevel current amplification of ZnO rods RRAM devices induced by UV light illumination is reported here for the first time. The resistive switching mechanism underlying in this type of devices is attributed to the formation of conductive filaments composed of oxygen vacancies. The analysis of the photodecay processes carried out on the devices fabricated with different electrodes shows that the type of interface (Ag/ZnO and Au/ZnO) affects the surface barrier height, which influences the photodecay rate. It is shown that by applying UV light, higher relaxation constants (slower photodecay rates) are obtained and lead to multilevel current amplification behavior.

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 science0.0000.000
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 head0.027
GPT teacher head0.282
Teacher spread0.254 · 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

Citations86
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicAdvanced Memory and Neural ComputingFrench-language works237,207