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Record W2028706881 · doi:10.1109/isscc.2015.7063017

13.6 A 600μW Bluetooth low-energy front-end receiver in 0.13μm CMOS technology

2015· article· en· W2028706881 on OpenAlexaff
Anith Selvakumar, Meysam Zargham, Antonio Liscidini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrical engineeringVoltage-controlled oscillatorSpurious-free dynamic rangeCMOSPhase noiseNoise figureRF front endLocal oscillatorSensitivity (control systems)BluetoothElectronic engineeringDissipationNoise (video)PhysicsResonatorComputer scienceEngineeringVoltageRadio frequencyTelecommunicationsWirelessAmplifier

Abstract

fetched live from OpenAlex

One of the main goals for the next generation of radios for wireless sensor and body-area networks (WSN and WBAN) is a sub-mW receiver (RX) compliant with energy-harvested supplies. In this direction, the Bluetooth standard has introduced a low-energy operative mode (BLE) with wider channel spacing (2MHz) and relaxed blocker tolerance. The minimum sensitivity required is −70dBm but even with a sensitivity 10dB lower the BLE receiver can have a noise figure close to 19dB [1]. Although linearity and noise specs have been significantly relaxed, the design of a sub-mW solution remains challenging since the power dissipation cannot be simply scaled with the spurious-free-dynamic-range (SFDR). In fact, the ultimate bound is set by the power burned in the voltage-controlled oscillator (VCO), which is used for the generation of the local oscillator (LO) necessary for the signal downconversion. Since, for a targeted phase noise, the current required by the VCO is inversely proportional to the quality factor of the resonator adopted, a straightforward approach is to use a high-Q tank like the FBAR used by Wang et al. [2]. However in low-cost CMOS processes, when high-Q resonators are not present, an alternative strategy is to share the VCO bias current with the other blocks of the RF front-end as in the LMV cell proposed by Tedeschi et al. [3].

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)0.001
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: none
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0290.054

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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Same topicRadio Frequency Integrated Circuit DesignFrench-language works237,207