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Record W2904188668 · doi:10.1109/antem.2018.8572914

Cryo-CMOS Low-Noise Amplifier for the Square Kilometre Array

2018· article· en· W2904188668 on OpenAlexaff
Alexander Sheldon, Leonid Belostotski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCMOSReturn lossLow-noise amplifierAmplifierElectrical engineeringNoise figureNoise (video)Y-factorMaterials sciencePhysicsOptoelectronicsNoise temperatureEngineeringPhase noiseComputer science

Abstract

fetched live from OpenAlex

This paper presents early results from the investigation of a cryogenically cooled CMOS (Cryo-CMOS) low-noise amplifier (LNA). The LNA was designed in a bulk 65-nm CMOS process. Measurements were performed at 300 K and a noise temperature (figure) of 12 K (0.18 dB) was achieved at 1420 MHz (the neutral hydrogen line). The amplifier also exhibits $\mathrm {a}32 \pm 1.65$ dB gain, an input return loss better than 8.3 dB, an output return loss better than 15.6 dB and consumes a total of 105 mW, 51 mA from a 0.7-V supply and 69 mA from a 1-V supply. From simulations, the LNA is expected to achieve a noise temperature (figure) of 4.5 K (0.07 dB) at an ambient temperature of 77 K while consuming 35 mW, 22mA from a 1-V supply and 19 mA from a 0.7-V supply.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.999

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)0.0010.001

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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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