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Record W2076808510 · doi:10.1116/1.1784826

Sub-100°C a-Si:H thin-film transistors on plastic substrates with silicon nitride gate dielectrics

2004· article· en· W2076808510 on OpenAlexaff
Andrei Sazonov, Christian McArthur

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2004
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePlasma-enhanced chemical vapor depositionThin-film transistorOptoelectronicsSubstrate (aquarium)FabricationDielectricSilicon nitrideTransistorSiliconChemical vapor depositionThreshold voltageNanotechnologyElectrical engineeringVoltageLayer (electronics)

Abstract

fetched live from OpenAlex

Fabrication on thin-film transistors (TFTs) on flexible plastic substrates for large-area imagers and displays has been made possible by lowering the deposition temperatures, which reduces the thermal deformation of plastic substrates, greatly facilitating substrate preparation and device patterning. Furthermore, at extremely low deposition temperatures, much wider variety of low-cost substrates, plastics or otherwise, are available for use. In this article, we report on a-Si:H TFTs fabricated at 75°C on glass and plastic substrates. The TFTs were fabricated using inverted–staggered topology, in a full wet etch process. The TFT structures consisted of 140nm of sputtered Mo for gate, 380nm of plasma-enhanced chemical vapor deposition (PECVD) a-SiNx:H gate dielectric optimized for 75°C, 50nm of PECVD a-Si:H channel material, 50nm of PECVD n+ a-Si:H for source∕drain contacts, and sputtered Al for contact metallization. Current–voltage characteristics were measured, and relevant transistor parameters were calculated. TFTs exhibited the leakage current below 10−12A, and on∕off current ratio exceeding 105. The results were compared to those for high temperature a-Si:H TFTs, and the differences are discussed.

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)
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 score0.191
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
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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.

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
Published2004
Admission routes1
Has abstractyes

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