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Record W4388795529 · doi:10.3390/mi14112118

Design and Analysis of a Low-Voltage VCO: Reliability and Variability Performance

2023· article· en· W4388795529 on OpenAlexaff
Tayebeh Azadmousavi, Ebrahim Ghafar‐Zadeh

Bibliographic record

VenueMicromachines · 2023
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsYork University
Fundersnot available
KeywordsVoltage-controlled oscillatorPhase noisedBcTransconductanceCMOSVoltageProcess cornersElectrical engineeringFigure of meritElectronic engineeringMaterials scienceTransistorEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

This paper investigates an adaptive body biasing (ABB) circuit to improve the reliability and variability of a low-voltage inductor-capacitor (LC) voltage-controlled oscillator (VCO). The ABB circuit provides VCO resilience to process variability and reliability variation through the threshold voltage adjustment of VCO's transistors. Analytical equations considering the body bias effect are derived for the most important relations of the VCO and then the performance is verified using the post-layout simulation results. Under a 0.16% threshold voltage shift, the sensitivity of the normalized phase noise and transconductance of the VCO with the ABB circuit compared to the constant body bias (CBB) decreases by around 8.4 times and 3.1 times, respectively. Also, the sensitivity of the normalized phase noise and transconductance of the proposed VCO under 0.16% mobility variations decreases by around 1.5 times and 1.7 times compared to the CBB, respectively. The robustness of the VCO is also examined using process variation analysis through Monte Carlo and corner case simulations. The post-layout results in the 180 nm CMOS process indicate that the proposed VCO draws a power consumption of only 398 µW from a 0.6 V supply when the VCO frequency is 2.4 GHz. It achieves a phase noise of -123.19 dBc/Hz at a 1 MHz offset and provides a figure of merit (FoM) of -194.82 dBc/Hz.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.728
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.211
Teacher spread0.201 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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