MétaCan
Menu
Back to cohort
Record W2322175827 · doi:10.1149/1.3633654

(Invited) ALD and AVD Grown Perovskite-type Dielectrics for Metal-Insulator-Metal Applications

2011· article· en· W2322175827 on OpenAlexaff
Christian Wenger, Mindaugas Lukosius, Tom Blomberg, A. Abrutis, P. K. Baumann, G. Ruhl

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceDielectricAtomic layer depositionMetal-insulator-metalCapacitorHigh-κ dielectricAmorphous solidTinThin filmMetalAnalytical Chemistry (journal)Chemical vapor depositionInsulator (electricity)ElectrodeOptoelectronicsNanotechnologyCrystallographyMetallurgyChemistryElectrical engineeringPhysical chemistryVoltage

Abstract

fetched live from OpenAlex

Atomic Vapor Deposition (AVD) and Atomic Layer Deposition (ALD) techniques were successfully applied for the depositions of perovskite type dielectrics, namely, Sr-Ta-O, Ti-Ta-O, Sr-Ti-O, Ba-Hf-O, Nb-Ta-O and Ce-Al-O. Thin films were investigated as alternative dielectrics for Metal-Insulator-Insulator (MIM) capacitors. Structural and electrical properties are investigated after depositing the metal oxides on 200 mm TiN/Si (100) substrates within the temperature range of 225-400 ºC. Electrical properties, investigated after sputtering Au top electrodes, revealed that the main characteristics are different for each dielectric. The highest dielectric constants were achieved for crystalline SrTiO3 (k=95), crystalline CeAlO3 (k = 60) and amorphous Ti-Ta-O (k = 50) films. However, Sr-Ta-O based MIM capacitors showed the lowest leakage current densities as well as the smallest capacitancevoltage linearity coefficients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.300
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.213
Teacher spread0.187 · 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 teacher head, 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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicSemiconductor materials and devicesFrench-language works237,207