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Record W1630830202 · doi:10.1063/1.1584925

Instability of the Noise Level in Polymer Field-Effect Transistors with Non-Stationary Electrical Characteristics

2003· article· en· W1630830202 on OpenAlexafffund
Ognian Marinov

Bibliographic record

VenueAIP conference proceedings · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsMcMaster UniversitySimon Fraser University
FundersSimon Fraser UniversityMcMaster University
KeywordsMaterials scienceNoise (video)PolymerLeakage (economics)Charge carrierTransistorCondensed matter physicsElectrodeElectron mobilityOptoelectronicsChemical physicsVoltageElectrical engineeringChemistryComposite materialPhysicsComputer science

Abstract

fetched live from OpenAlex

The low frequency noise (LFN) properties of field‐effect transistors (FETs) using polymers as the semiconducting substrate material are investigated and explained in terms of the charge carrier transport in polymer thin‐film structure. Three mechanisms contribute to the carrier transport — charge injection from source electrode into polymer, charge hopping between polymer molecules for drift transport toward the drain, and charge buildup, probably at polymer‐oxide interface. Charge buildup is responsible for non‐stationary electrical characteristics, but does not contribute significantly to the LFN. Charge hopping determines the maximum value of the mobility and the minimum value of mobility 1/f noise. The variations of the PFET characteristics are mainly due to dispersion in the injection barrier of source‐to‐polymer contact. High disorder in the polymer at the source contact can increase the leakage current in PFET and can introduce number fluctuation SGN in the polymer conduction on top of the mobility fluctuation. SGN is proportional to the DC power applied to the injection barrier and should be assumed as a voltage source, since carrier hopping in the polymer reduces the effect of the injection noise. At present, the physical origin of SGN is not fully understood.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.435

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 head0.017
GPT teacher head0.222
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 designObservational
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

Citations3
Published2003
Admission routes2
Has abstractyes

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