MétaCan
Menu
Back to cohort
Record W2338178895 · doi:10.1109/led.2016.2557231

Multilayer MoS2 Thin-film Transistors Employing Silicon Nitride and Silicon Oxide Dielectric Layers

2016· article· en· W2338178895 on OpenAlexafffund
Czang-Ho Lee, Nicholas Vardy, William S. Wong

Bibliographic record

VenueIEEE Electron Device Letters · 2016
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDielectricPlasma-enhanced chemical vapor depositionMaterials scienceSiliconGate dielectricAnalytical Chemistry (journal)OptoelectronicsTransistorPhysicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Bottom-gate molybdenum disulfide (MoS2) TFTs, having plasma-enhanced chemical vapor deposition (PECVD) gate-dielectric thin films, were fabricated from 80-100 nm thick mechanically exfoliated MoS2multilayers. Three separate gate dielectric structures were investigated consisting of: 1) a single PECVD SiNxlayer; 2) a dual PECVD SiOx/SiNxlayer; and 3) a reference thermal SiO2 layer. The devices fabricated on the bi-layer dielectric had a field-effect mobility (μe) of ~12 cm2/Vs and an ON/OFF ratio of ~105, while the TFTs on SiNxor thermal SiO2dielectric had μe~ 4 to 5 cm2/Vs and an ON/OFF ratio ~104to 105. The calculated interface trap density for the bi-layer dielectric devices was found to be 10× less than the SiNxdevices and 2x less than the thermal SiO2devices. The variation in the defect density was due to localized trap states in the dielectric/semiconductor interface.

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 score0.003

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 communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Electron Device LettersSame topic2D Materials and ApplicationsFrench-language works237,207