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Record W2114548500 · doi:10.1109/ccece.2005.1557439

Low frequency noise in amorphous silicon thin-film transistors

2006· article· en· W2114548500 on OpenAlexafffund
Kanghyun Kim, Robert E. Johanson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Waterloo
KeywordsThin-film transistorNoise (video)Amorphous siliconTransistorMaterials scienceSiliconOptoelectronicsAmorphous solidNoise powerDetectorSaturation (graph theory)Flicker noiseJohnson–Nyquist noisePhysicsElectrical engineeringPower (physics)VoltageComputer scienceNanotechnologyNoise figureOpticsCrystallographyCMOSChemistryEngineeringCrystalline siliconArtificial intelligenceQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

Hydrogenated amorphous silicon thin-film transistors (a-Si:H TFTs) are used as charge switches in flat-panel X-ray detectors. The inherent noise in the TFTs contributes to the overall noise figure of the detectors and degrades the image quality. Measurements of the noise provide an important parameter for modeling the performance of the detectors and contribute to understanding the origins of the noise that could lead to improvements in TFT design. We have measured the low-frequency, conductance noise in a-Si:H TFTs with an inverted staggered structure. We find that the noise power density spectrum fits well to a 1/f <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> power law with a near one. The noise power is inversely proportional to the gate voltage and also inversely proportional to channel length in both the linear and saturation regions. The results are in accord with theory. The Hooge parameter for the devices is approximately 1xW <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup>

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 score0.292
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.176
Teacher spread0.170 · 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

Citations1
Published2006
Admission routes2
Has abstractyes

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