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A Compact, High Tuning Accuracy and Enhanced Linearity 37-43 GHz Digitally-Controlled Vector Sum Phase Shifter

2024· article· en· W4391992555 on OpenAlexafffund
Mehran Hazer Sahlabadi, Hang Yu, Jingjing Xia, Slim Boumaiza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhase shift moduleLinearityComputer sciencePhase (matter)Electronic engineeringOptoelectronicsMaterials sciencePhysicsTelecommunicationsEngineeringMicrowave

Abstract

fetched live from OpenAlex

This paper presents a compact digitally controlled vector sum phase shifter (DC-VSPS) capable of precise gain and phase control across a wide bandwidth. The DC-VSPS utilizes a pair of differential variable gain amplifiers (VGAs) designed to optimize gain and phase tuning accuracy. Moreover, it incorporates differential transformers-based output combiner and input quadrature hybrid, enhancing integration, bandwidth, and matching performance. A 6-bit DC-VSPS prototype was successfully designed and fabricated using the 45 nm silicon-on-insulator (SOI) CMOS technology, occupying a minimal core area of $0.084 \mathrm{~mm}^{2}$. Experimental results demonstrate an excellent tuning range of 360° for phase control with a resolution of 6 bits and 14 dB for gain control with 1 dB resolution. Furthermore, comprehensive measurements indicate excellent performance over the frequency range of 35-43 GHz, with total root-mean-square (RMS) phase error, total RMS gain error, and group delay variation all within 1.5°, 0.24 dB, and ±12 picoseconds, respectively. The prototype also exhibits input-referred 1dB gain compression power levels exceeding 4 dBm and input-output return losses better than 9 dB across the entire bandwidth.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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