A 430-mA Capacitor Less Analog Assisted Hybrid LDO With Fast Transient Algorithm
Bibliographic record
Abstract
This paper presents an analog-assisted hybrid low-dropout regulator (LDO) with a wide load current range and fast transient response. The proposed design is composed of digital and analog loops of which the transient response is dictated by the digital portion. A fast approximation algorithm leveraging charge distribution reduces settling time significantly compared to linear and SAR approaches. A wide load range droop detector further improves transient response with negligible power overhead. The analog assisted circuits continuously provide current in response to the load current change without disturbing the loop dynamic. Implemented in a TSMC 180-nm standard CMOS technology, the LDO supports a 430 mA maximum load and ensures loop stability without external capacitors. It achieves a 480 mV undershoot at 430 mA with 100 ns edge time, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$49~\mu $ </tex-math></inline-formula>A quiescent current, and settling times of 225 ns and 260 ns for undershoot and overshoot at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C_{L}=0$ </tex-math></inline-formula> pF when a 10-MHz clock is used. Measurement shows that activating the droop detector reduces undershoot by 54%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".