A New Digital Adaptive Voltage Positioning Technique with Dynamically Varying Voltage and Current References
Bibliographic record
Abstract
A digital adaptive voltage positioning (digital AVP) technique with fast transient response for voltage regulators (VRs) is proposed in this paper. The proposed digital control architecture operates with fixed-frequency peak current mode control. Two digital-to-analog converters (DACs) are used instead of analog-to-digital converters (ADCs), thus significantly reducing system complexity. The control law is straightforward and no compensator is involved in the control loop, which greatly reduces the computation delay. Both the voltage and current references are changed dynamically at DAC clock frequency to achieve fast transient response. Furthermore, dynamic reference step adjustment method is employed to reduce the high-speed requirement on reference updating clock without compromising steady-state performance. Nonlinear control, including operation state recognition, multimode operation, decision-making and multiturn-on/turn-off control schemes, is used to minimize the transient-assertion-to-action delay and maximize the inductor current slew rate. Steady-state analysis was performed to demonstrate the digital controller operation. Finally, a two-phase 12- to 1-V, 40-A, 250-kHz synchronous buck converter with the proposed digital controller was designed to verify the theoretical analysis by simulation and experimental results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".