Architectural Advancement of Digital Low-Dropout Regulators
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".