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Record W3021000812 · doi:10.1021/acsanm.0c00173

Passive Filters for Nonvolatile Storage Based on Capacitive-Coupled Memristive Effects in Nanolayered Organic–Inorganic Heterojunction Devices

2020· article· en· W3021000812 on OpenAlexafffund
Shubham Ranjan, Bai Sun, Guangdong Zhou, Yimin A. Wu, Lan Wei, Y. Zhou

Bibliographic record

VenueACS Applied Nano Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCapacitive sensingMaterials scienceOptoelectronicsNanodeviceMemristorVoltageFilter (signal processing)Power (physics)NanotechnologyElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

It is well-known that the reprogrammable device is one of the important needs for circuit design. In this paper, nanolayered TiO 2 and maple leaves (ML) are combined to form a functional layer (TiO 2 -ML) inside memristive devices, which demonstrate both the capacitive effect and the nonvolatile storage capability. When the voltage increases from zero, the device first enters a capacitive-coupled memristive state at low voltage before switching to a normal memristive state at a higher voltage. The existence of the capacitive behavior results in a nonzero-crossing I – V characteristic different from the zero-crossing curve observed in normal memristive device. Utilizing this capacitive-coupled memristive behavior, we design a low power passive filter with applications toward reprogrammable analog circuit designs, paving a path toward a multifunctional nanodevice in the future.

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 communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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
Published2020
Admission routes2
Has abstractyes

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Same venueACS Applied Nano MaterialsSame topicAdvanced Memory and Neural ComputingFrench-language works237,207