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Record W3154062942 · doi:10.1109/led.2021.3072000

Low-Frequency Noise Characteristics of Inkjet-Printed Electrolyte-Gated Thin-Film Transistors

2021· article· en· W3154062942 on OpenAlexaff
Xiaowei Feng, Surya Abhishek Singaraju, Hongrong Hu, Gabriel Cadilha Marques, Tongtong Fu, Peter Baumgartner, Daniel Secker, Mehdi B. Tahoori, Jasmin Aghassi‐Hagmann

Bibliographic record

VenueIEEE Electron Device Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
FundersMinisterium für Wissenschaft, Forschung und Kunst Baden-WürttembergBundesministerium für Bildung und Forschung
KeywordsTransistorMaterials scienceThin-film transistorNoise (video)OptoelectronicsFlicker noiseCapacitanceGate dielectricElectrical engineeringNanotechnologyVoltageNoise figureComputer scienceElectrodeLayer (electronics)PhysicsCMOSEngineering

Abstract

fetched live from OpenAlex

Low-frequency noise is a critical characteristic of transistors, but there are only a few experimental works on the noise in printed electronics. In this work, we characterize the low-frequency noise of inkjet-printed electrolyte-gated thin-film transistors (EGTs) with indium-oxide semiconductors. We confirm that the carrier number fluctuation with correlated mobility fluctuation is the dominating noise generation mechanism. Also, we present the benchmark analysis on the noise level of various thin-film technologies. Notably, the extracted value of trap density near the insulator-channel interface is high, indicating an inferior quality of solution-processed and inkjet-printed thin-films. However, because of electrolyte-gating, the large areal gate capacitance compensates the negative effect of the high trap density, effectively reducing the flat-band voltage noise. As a result, the normalized drain current noise is considerably lower than solution-processed transistors and comparable with sputtered inorganic transistors with dielectric gating. This renders the electrolyte-gating approach useful in reducing the noise for printed/solution-based transistors, suitable for low-noise applications.

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 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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.197
Teacher spread0.190 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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