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Record W3025714321 · doi:10.1149/ma2020-0110819mtgabs

Suppressing 1/f Noise in Graphene Transistors By Area Scaling

2020· article· en· W3025714321 on OpenAlexaff
Minh Tran, Alba Centeno, Amaia Zurutuza, Ilargi Napal, Thomas Szkopek

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneFlicker noiseMaterials scienceNoise (video)OptoelectronicsField-effect transistorTransistorContact resistanceChemical vapor depositionGraphene nanoribbonsParyleneNanotechnologySubstrate (aquarium)Noise figurePhysicsCMOSVoltage

Abstract

fetched live from OpenAlex

Flicker noise or 1/f noise refers to processes in which the power spectral density (PSD) is inversely proportional to frequency and is typically the dominant noise source in transistors at low frequency. We report our work on measurements of unprecedentedly low 1/f noise in graphene field effect transistors, which we attribute to large device area. Large area graphene grown by chemical vapor deposition (CVD) has potential for a variety of applications including biomolecular sensors, bolometric photodetectors and ion sensitive field effect transistors (ISFET) [1]. For all these applications, low-frequency 1/f noise is found to be the dominant factor that determines sensor resolution limits. Hence, the absolute value of 1/f noise PSD serves as a crucial performance metric for graphene sensor applications. Previous studies of 1/f noise in graphene devices has been performed using both CVD grown graphene and exfoliated graphene. Balandin et al [2] has studied 1/f noise in exfoliated graphene and how it varies with substrate and charge carrier concentration. Karnatak et al [3] has shown that contact resistance can play a dominant role in 1/f noise. To date, there has been no experimental study of 1/f as the graphene channel is scaled up to mm lengths. We report here our work on 1/f noise measurement of graphene field effect transistors with varying channel and contact geometries. In our experiments, CVD grown graphene on copper was transferred onto fused silica coated with 100 nm of parylene using a standard wet transfer process. Parylene was used as an interface between the graphene and fused silica, which has been shown by Fakih et al [1] to reduce both drift and hysteresis in graphene ISFETs. The transferred graphene was processed with two photolithography steps to fabricate devices with areas ranging from 12µm2 to 36mm2. Electrical contact to the graphene was made directly with Au. The 1/f noise was measured by first applying a dc bias using a low noise lithium-ion battery, and the generated voltage fluctuations were then measured using a low noise voltage pre-amplifier and a 24-bit digitizer. The voltage PSD of 1/f noise in graphene, will typically have the form of SV=Vo 2K/f. Where Vo is the dc voltage applied across the device, f is the frequency, and K is the unitless noise constant. K is experimentally approximated to be (1/N)∑nSVnfn/Vo 2, where SVn is the voltage PSD measured at n different frequencies fn. The work from Balandin et al [2] demonstrates typical K values of 10-8, and work from Karnatak et al [3] shows values as low as 10-9. Our devices have unprecedented low measured K values of 10-14, which we attribute to the large device area of 36 mm2. Our results experimentally demonstrate that the noise parameter can be decreased by orders of magnitude by working with large area devices. Our work suggests that for graphene based sensor applications, large device area is favourable for improved sensor resolution. References: 1. Fakih, I., et al., High resolution potassium sensing with large-area graphene field-effect transistors. Sensors and Actuators B: Chemical, 2019. 291: p. 89-95. 2. Balandin, A.A., Low-frequency 1/f noise in graphene devices. Nature nanotechnology, 2013. 8(8): p. 549-55. 3. Karnatak, P., et al., Current crowding mediated large contact noise in graphene field-effect transistors. Nature Communications, 2016. 7(1).

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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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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