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A 58 nW ± 35 ppm/°C Oscillator for IoT Battery-less Sensor Applications

2020· article· en· W3117772515 on OpenAlexafffund
Milad Salehi, Mohamed R. Ali, Yvon Savaria, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
FundersCMC Microsystems
KeywordsSensitivity (control systems)Battery (electricity)CMOSElectrical engineeringPower (physics)Antenna (radio)Electronic engineeringLow-power electronicsRangingSIGNAL (programming language)Computer scienceWireless sensor networkRadio frequencyMaterials scienceEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

We present in this paper a battery-less sensor structure with energy harvesting in which an antenna is used for both collecting the RF signals and as a sensing element. Also, an ultra-low-power oscillator with low sensitivity to temperature variations has been designed to generate a clock signal for the proper operation of the proposed sensor design. This oscillator is intended for ultra-low power and low-frequency applications. It consumes only 58 nW of power from a 1 V supply while occupying 190 μm x 120 μm of silicon area. In addition, it shows temperature sensitivity of ± 35 ppm/°C over temperatures ranging from -40 to 85 °C. The proposed structure has been designed and simulated with a 0.18 μm standard CMOS technology. The proper functionality of the presented design has been validated through post-layout simulation results.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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