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Record W4384340581 · doi:10.1002/adfm.202305733

3D HfO<sub>2</sub> Thin Film MEMS Capacitor with Superior Energy Storage Properties

2023· article· en· W4384340581 on OpenAlexafffund
Yijun Zhang, Huifeng Zhao, Tianyi Yan, Wei Ren, Gang Niu, Zhuangde Jiang, Zenghui Liu, He‐Ping Wu, Zhe Wang, Zuo‐Guang Ye

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsSimon Fraser University
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsCapacitorMaterials scienceMicroelectronicsMicroelectromechanical systemsEnergy storageOptoelectronicsFilm capacitorDielectricElectrical engineeringVoltagePower (physics)Engineering

Abstract

fetched live from OpenAlex

Abstract Capacitors are ubiquitous and crucial components in modern technologies. Future microelectronic devices require novel dielectric capacitors with higher energy storage density, higher efficiency, better frequency and temperature stabilities, and compatibility with integrated circuit (IC) processes. Here, in order to overcome these challenges, a novel 3D HfO 2 thin film capacitor is designed and fabricated by an integrated microelectromechanical system (MEMS) process. The energy storage density (ESD) of the capacitor reaches 28.94 J cm −3 , and the energy storage efficiency of the capacitor is up to 91.3% under an applied electric field of 3.5 MV cm −1 . The ESD can be further improved by reducing the minimum period structure size of the 3D capacitor. Moreover, the 3D capacitor exhibits excellent temperature stability (up to 150 °C) and charge‐discharge endurance (10 7 cycles). The results indicate that the 3D HfO 2 thin film MEMS capacitor has enormous potential in energy storage applications in harsh environments, such as pulsed discharge and power conditioning electronics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.195
Teacher spread0.180 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2023
Admission routes2
Has abstractyes

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