Comparison and Design of Linear and Exponential Integrated Charge Pumps
Bibliographic record
Abstract
This paper presents static and dynamic models for linear and exponential integrated charge pumps in both step-up and step-down modes. The static models are used to compare the slow-switching and fast-switching output resistance of various configurations, considering optimized and non-optimized capacitors and switches. In the dynamic models, the self-loading capacitance is determined using a simpler approach than previous works, allowing for a more straightforward comparison of the start-up time and charging efficiency. To highlight the differences between linear and exponential charge pumps, the working voltages of capacitors and switches are calculated, with these expressions guiding the selection of the most appropriate devices for each configuration. Additionally, parasitic capacitances and leakage currents are modeled and analyzed across the circuit configurations, and their impact on overall efficiency is assessed. The procedure for optimally sizing capacitors and switches using different device types is then discussed. Finally, two design examples in 65-nm CMOS technology are presented to validate the models, demonstrate design procedures, and highlight the advantages and limitations of practical implementations of each circuit.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".