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Record W2028641983 · doi:10.1063/1.2927485

Polymer thin film transistor without surface pretreatment on silicon nitride gate dielectric

2008· article· en· W2028641983 on OpenAlexaff
Flora M. Li, Parul Dhagat, Hanna M. Haverinen, Iain McCulloch, Martin Heeney, Ghassan E. Jabbour, Arokia Nathan

Bibliographic record

VenueApplied Physics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThin-film transistorMaterials scienceDielectricGate dielectricOptoelectronicsSilicon nitrideTransistorFabricationNitrideSiliconNanotechnologyElectrical engineeringLayer (electronics)

Abstract

fetched live from OpenAlex

It is well known that surface modification of the gate dielectric in organic thin film transistors (TFTs) plays an important role in device performance, often giving rise to severalfold improvements in field-effect mobility. This paper reports on solution-processed polymer TFTs with mobilities comparable to high performance counterparts despite the absence of dielectric surface pretreatment. An effective mobility of 0.1cm2∕Vs was obtained with poly(2,5-bis(3-dodecylthiophene-2-yl)thieno[3,2-b]thiophene) transistors on silicon nitride gate dielectric. The results indicate that by judicious preparation of the device layers, one can mitigate the need for dielectric surface pretreatment, thereby reducing fabrication complexity without compromising TFT performance.

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 score0.006

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)0.0020.001

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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