Temperature-Stable Low-Power RF-to-DC Dickson Charge Pump Rectifiers for Battery-Free Sensing and IoT Systems
Bibliographic record
Abstract
Temperature variation poses a significant challenge for battery-free sensors and Internet of Things (IoT) systems, mainly due to the absence of built-in temperature compensation modules. This work presents a strategy to identify Schottky diodes for low-power RF-to-dc Dickson charge pump (DCP) rectifiers to enhance temperature stability. Theoretical analysis pinpoints that performance degradation in dynamic temperatures results from the mismatch loss between diode nonlinear junction resistance and load resistance. The analytical method is implemented to synthesize the optimum number of stages and identify suitable Schottky diodes for low-power RF-to-dc DCP rectifiers. Experimental measurements demonstrate that the SMS7621-based 3-stage RF-to-dc DCP rectifier maintains a wide matched operating temperature range from$- 32.5~^{\circ }$C to$70~^{\circ }$C. Further experiments show that its dc output voltage remains above 3.2 V across a wide temperature range of$- 40~^{\circ }$C to$80~^{\circ }$C when the RF input is −8 dBm, which can drive a commercial wireless sensor board. This work aims to serve as a benchmark for developing reliable low-power RF-to-dc DCP rectifiers that meet various operating temperature requirements of battery-free IoT sensors.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".