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A 0.6-V 108-nW 100-kHz Sub-Threshold Delay-Locked Loop with Digital Linearization for Low-Power SAR ADC

2024· article· en· W4402753432 on OpenAlexaff
Wenhao Wu, Fei Yuan, Yushi Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsLakehead UniversityToronto Metropolitan University
Fundersnot available
KeywordsLinearizationLoop (graph theory)Power (physics)Electronic engineeringSuccessive approximation ADCControl theory (sociology)Computer scienceElectrical engineeringPhysicsEngineeringCapacitorMathematicsVoltageNonlinear system

Abstract

fetched live from OpenAlex

This paper investigates the instability of low-power low-frequency charge-pump delay-locked loops (DLL) operating in sub-threshold for low-power SAR ADC. We show the charge leakage of the loop-filter capacitor during the long idle state of the phase detector causes the control voltage of the DLL to drift. Although the amount of voltage drift is insignificant, the exponential relation between the delay and control voltage of the delay stage operating in sub-threshold results in a significant change in the delay of the DLL, causing the DLL to oscillate. To combat this, we propose a counter-based sub-threshold DLL with digital linearization. The delay stages of the DLL are linearized using a digital-to-analog converter (DAC) with its bit weights set as per the characteristics of the delay stage such that the relation between the delay and control voltage of the compensated delay stage is linear. Designed in a TSMC 130 nm 1.2 V CMOS technology with a reduced supply voltage of 0.6 V, the DLL locks to a 100 kHz 50% duty-cycle external reference at FF/-20°C, TT/27°C, and SS/60°C process/temperature corners and consumes 108.6 nW at TT/27°C. The proposed DLL offers the distinct characteristics of intrinsic stability, ultra-low power consumption, and excellent compatibility with technology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.201
Teacher spread0.192 · 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 designSimulation or modeling
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

Citations7
Published2024
Admission routes1
Has abstractyes

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