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Record W3046131452 · doi:10.1109/access.2020.3012467

Architectural Advancement of Digital Low-Dropout Regulators

2020· article· en· W3046131452 on OpenAlex
Muhammad Abrar Akram, In-Chul Hwang, Sohmyung Ha

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
FundersInstitute for Information and Communications Technology PromotionMinistry of Science and ICT, South KoreaIran Telecommunication Research CenterNational Research Foundation of KoreaInformation Technology Research CentreMinistry of Science, ICT and Future Planning
KeywordsComputer scienceScalabilityTransient (computer programming)CapacitorPower managementPower (physics)TransistorVoltageElectronic engineeringEmbedded systemElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Digital Low-dropout (DLDO) regulators have been widely utilised for highly-efficient fine-grained power delivery and management in system-on-chips (SoCs) due to their process scalability, ease of integration, and low-voltage operation. However, conventional DLDOs suffer gravely from the power-speed tradeoff, which arises from the use of sampling clocks. To obtain reasonable performance in the undershoot and recovery during load transient states, a large output capacitor is inevitably required in these DLDOs. Moreover, they inherently involve large steady-state voltage ripples and poor power-supply rejection (PSR). These limitations of synchronous DLDOs and their counter measures are thoroughly discussed in this paper. Various design strategies of major building blocks, i.e. comparators and power transistor arrays, are explained in detail with examples. Architectural advances are also expounded including state-of-the-art DLDO architectures such as clock-boosted synchronous, analog-assisted synchronous, asynchornous, event-driven, and hybrid DLDOs. These state-of-the-art DLDOs do not only address the power-speed tradeoff and achieve fast load transient responses, but also can eliminate the use of an output capacitor in some cases. Moreover, some hybrid DLDOs successfully removed the steady state ripples and achieve high PSR. All of these DLDO are compared on basis of their performance metrics and figure-of-merits (FOMs).

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.453

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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