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Enhanced Accuracy in On-Wafer Noise Figure Measurements at Sub-Terahertz Frequencies

2024· article· en· W4401113791 on OpenAlexaff
Nizar Messaoudi, Shengjie Gao, Muhammad Waleed Mansha, Y. Baeyens, Mustafa Sayginer, Slim Boumaiza, Bryan Hosein, Shahriar Shahramian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of WaterlooFocus Microwaves (Canada)
Fundersnot available
KeywordsTerahertz radiationWaferMaterials scienceNoise (video)OptoelectronicsNoise figureOpticsAcousticsPhysicsComputer scienceCMOS

Abstract

fetched live from OpenAlex

This paper delves into the precise on-wafer measurement of the noise figure (NF) of active circuits operating at millimetre-wave and sub-Terahertz frequencies. The focus lies on addressing the inherent challenge of the source impedance deviation at the input port plane of the die-under-test (DUT) from a matched load, a factor that significantly impacts the accuracy of NF measurements. The proposed testing system features an automated source tuner designed to counteract the non-idealities of ancillary components, including probes, cables, noise sources, switches, adapters, and frequency extenders, contributing to the impedance deviation. The testing system enables the measurement of the noise parameters of the DUT, allowing for the subsequent deduction of its noise figure. To validate its efficacy, the designed testing system is applied to measure the NF of a D-band distributed low-noise amplifier. The obtained results affirm the superior accuracy of the designed testing system when compared to the conventional Y-factor method.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.371
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.035
GPT teacher head0.246
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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