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Record W4412078816 · doi:10.1080/02564602.2025.2522079

0.35 µm CNTFET for Multi-band Low Noise Amplifier

2025· article· en· W4412078816 on OpenAlexaff
M. S. Alam, A. Mukherjee

Bibliographic record

VenueIETE Technical Review · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsComputer scienceLow-noise amplifierNoise (video)AmplifierTelecommunicationsElectrical engineeringCarbon nanotube field-effect transistorOptoelectronicsMaterials scienceBandwidth (computing)TransistorEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The potential features of carbon nanotube field-effect transistors (CNTFETs) for designing RF circuit, e.g. a low-noise amplifier (LNA) circuit is demonstrated. However, existing studies have typically focused on LNAs operating in a single band, without extensive investigation into small- and large-signal performances. These circuits should ideally be operated in multi-band to reduce circuit complexity and power consumption with stable operation in the frequency of interest. Therefore, to address these gaps, this paper proposes a dual-band CNTFET LNA designed for 900/1800MHz GSM with a detailed analytical analysis of S-parameters, noise figure, and non-linearity quantified in terms of third-order intercept for the first time. Using these characteristics, a new figure-of-merit is established for comparison with state-of-the-art CMOS technology. Thus this paper addresses the existing research gap in the literature for dual-band CNTFET LNA circuit design.

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 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)0.000
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
Metaresearch0.0000.000
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.339
Teacher spread0.298 · 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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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