A High-Speed Capacitor Less LDO with Multi-Loop Fast Feedback and Bandwidth Enhancement Control
Bibliographic record
Abstract
This paper presents a high-speed low dropout (LDO) regulator with wide dynamic range. The use of piecewise speed enhancement technique dividing the loop dynamic into three phases in which the current regulation circuits (CRC), large-signal derivative path control circuits addressing the design challenge of slew rate limitation, and the hybrid passive-active frequency compensation (PAFC) for small signal settling time improvements are introduced lends the proposed LDO to providing constant output voltage under the condition of large load variations. The LDO is designed in TSMC 180-nm 1.8 V standard CMOS technology with 0.17 mm2 active area. The quiescent current is 380 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu \mathrm{A}$</tex> at no load. With regulated 1.2 V output, the input voltage ranges from 1.3 V to 1.8 V. The measured overshoot and undershoot with load steps of 0 to 100 mA at 50 ns edge time are 135 mV and 105 mV, respectively. The settling time at 25 mA, 50 mA and 100 mA are 2.6 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu \mathrm{s}, 4.5\mu \mathrm{s}$</tex> , and 9.8 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu \mathrm{s}$</tex> , respectively. The LDO is competent in handling a wide range of output capacitance from 0 to 5 nF while the overshoot and undershoot exhibits small variation in the load step response.
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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) | 0.000 | 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".