Variable output, soft-switching DC/DC converter for VLSI dynamic voltage scaling power supply applications
Bibliographic record
Abstract
The implementation of a low-voltage zero-voltage-switching quasi-square-wave (ZVS-QSW) buck converter capable of meeting the future challenges of low-voltage VRMs is presented. By eliminating switching losses, high-efficiency operation at switching frequencies beyond 1 MHz is achieved. The design uses novel high-speed dead-time-locked-loops with fast dead-time error rejection to ensure zero-voltage-switching under dynamic loads and variable output conditions. The ZVS-QSW converter, which was implemented in a mixed-signal 0.18 m CMOS process, has a measured efficiency of 82% at 5 MHz with a 1.4 V output. The ZVS-QSW converter is intended to supply the next generation VLSI chips with a variable supply voltage for dynamic voltage scaling (DVS) applications. DVS refers to the real-time scaling of the supply voltage to the VLSI chip to minimize dynamic power consumption, while satisfying a variable target clock frequency. Several DVS strategies are examined, and it is shown that DVS can be applied to the ZVS-QSW converter using a dual-mode configuration. An experimental DVS test-bench was developed using a state-of-the-art Xilinx CPLD capable of operating from 1.35 V to 1.8 V. The PID controlled DVS system achieves the maximum V/sub DD/ transition in 22 /spl mu/s.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".