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A 81.2-83.1 GHz Differential Pulsed Millimeter Wave Voltage-Controlled Oscillator on a 65 nm CMOS Process for Radar and Imaging Applications

2024· article· en· W4408702188 on OpenAlexaff
Samiyalu Usurupati, Immanuel Raja, Chinmoy Saha, Yahia M. M. Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsExtremely high frequencyCMOSRadarMaterials scienceVoltageOptoelectronicsProcess (computing)Radar imagingMillimeterDifferential (mechanical device)Electrical engineeringPhysicsOpticsComputer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A differential pulsed millimeter wave (mm-wave) voltage-controlled oscillator (VCO) is proposed and designed on a 65 nm 1 -poly 10 -metal ($1 \mathrm{P10M}$) CMOS process for radar and imaging applications. The features of VCO and pulsed oscillators are integrated together. The losses offered by the on-chip inductor are compensated with a widely used NMOS cross-coupled pair by providing a sufficient amount of negative resistance-the feature of pulsed RF oscillations achieved by using a PMOS head switch. The controllability of the oscillation frequency is achieved by using tuning transistors. The tuning range of the proposed pulsed RF VCO is 1.9 GHz, from 81.2-83.1 GHz. The input pulse signal provides bias to the PMOS head switch when its amplitude is zero volts. The proposed pulsed RF VCO occupies an area of $\mathbf{1 7 5} \mathbf{u m} \times 130 \mathrm{um}$. The proposed design results in a phase of $\mathbf{- 8 7. 2 1 ~ d B c / H z}$ at $1 \mathbf{M H z}$ offset for the oscillation frequency of 83.1 GHz. The DC power consumption of the design varies from $8.6-11.6 \mathrm{~mW}$.

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 score0.002
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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