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Record W2277318313 · doi:10.1002/adfm.201504377

Contact Doping for Vertical Organic Field‐Effect Transistors

2015· article· en· W2277318313 on OpenAlexaff
Alrun A. Günther, Michael Sawatzki, Petr Formánek, Daniel Kasemann, Karl Leo

Bibliographic record

VenueAdvanced Functional Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsDopantMaterials sciencePentaceneOrganic field-effect transistorDopingTransistorField-effect transistorOptoelectronicsElectrodeLayer (electronics)Organic electronicsOrganic semiconductorNanotechnologyThin-film transistorElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Doping is a powerful tool to overcome contact limitations in short‐channel organic field‐effect transistors (OFETs) and has been successfully used in the past to improve the charge carrier injection in OFETs. The present study applies this familiar concept to the architecture of vertical organic field‐effect transistors (VOFETs), which are often severely limited by injection due to their very short channel lengths. The present study shows that the performance of p‐type VOFETs with pentacene as an active material can be significantly enhanced by the addition of the common p‐dopant C 60 F 36 as a thin injection layer underneath the VOFET source electrode, resulting in an increase of On‐state current and On/Off ratio by one order of magnitude. The present study further investigates mixed injection layers of pentacene and the p‐dopant and finds that the improvement is less pronounced than for the pure dopant layers and depends on the concentration of dopant molecules in the injection layer. Through application of the transfer length method to equivalent OFET geometries, the present study is finally able to link the observed improvement to a decrease in transfer length and can thus conclude that this length is a crucial parameter onto which further improvement efforts have to be concentrated to realize true short‐channel VOFETs.

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 score0.025
Threshold uncertainty score0.530

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations85
Published2015
Admission routes1
Has abstractyes

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