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Record W2969594826 · doi:10.1021/acs.nanolett.9b02683

A Unified Capacitive-Coupled Memristive Model for the Nonpinched Current–Voltage Hysteresis Loop

2019· article· en· W2969594826 on OpenAlexafffund
Bai Sun, Yuanzheng Chen, Ming Xiao, Guangdong Zhou, Shubham Ranjan, Wentao Hou, Xiaoli Zhu, Yong Zhao, Simon A. T. Redfern, Y. Zhou

Bibliographic record

VenueNano Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
FundersMinistry of Education of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsMemristorHysteresisCapacitive sensingCapacitorResistorNonlinear systemVoltagePhysicsControl theory (sociology)Topology (electrical circuits)Materials scienceCondensed matter physicsComputer scienceElectrical engineeringEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

The concept of the memristor, a resistor with memory, was proposed by Chua in 1971 as the fourth basic element of electric circuitry. Despite a significant amount of effort devoted to the understanding of memristor theory, our understanding of the nonpinched current–voltage ( I – V ) hysteresis loop in memristors remains incomplete. Here we propose a physical model of a memristor, with a capacitor connected in parallel, which explains how the nonpinched I – V hysteresis behavior originates from the capacitive-coupled memristive effect. Our model replicates eight types of characteristic nonlinear I – V behavior, which explains all observed nonpinched I – V curves seen in experiments. Furthermore, a reversible transition from a nonpinched I – V hysteresis loop to an ideal pinched I – V hysteresis loop is found, which explains the experimental data obtained in C 15 H 11 O 6 -based devices when subjected to an external stimulus (e.g., voltage, moisture, or temperature). Our results provide the vital physics models and materials insights for elucidating the origins of nonpinched I – V hysteresis loops ascribed to capacitive-coupled memristive 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.243
Teacher spread0.219 · 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

Citations201
Published2019
Admission routes2
Has abstractyes

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